AutoBuild Instrumentation Guide¶

Version: 1.1.0 Last Updated: 2026-04-11 Compatibility: GuardKit v1.0+, AutoBuild with instrumentation (FEAT-INST) Document Type: Technical Reference

Table of Contents¶

Overview
Architecture
Event Types Reference
File Locations
How to Use
Prompt Profiles and Digests
Template Pattern Context
Adaptive Concurrency
vLLM / Local Backend Specifics
Secret Redaction
Bootstrap Hard-Fail Gate (bootstrap_failure_mode)
Coach Independent Test Execution (coach.test_execution)
Troubleshooting
If AutoBuild stalls immediately

Overview¶

AutoBuild instrumentation provides structured observability across the entire Player-Coach pipeline. Every LLM call, tool execution, task lifecycle event, wave completion, and Graphiti query is captured as a typed event and persisted to a local JSONL file.

Why instrumentation exists:

Cost tracking — Token counts per call let you calculate spend per task and per feature.
Performance analysis — Latency metrics (latency_ms, ttft_ms) reveal bottlenecks in LLM calls and tool executions.
A/B prompt comparison — The prompt_profile field tags each call so you can compare digest-only vs digest+rules_bundle strategies.
Failure diagnosis — Structured failure_category fields enable grouping and triaging failures without reading logs.
Adaptive concurrency — Wave completion events feed the ConcurrencyController which auto-tunes worker count based on rate limits and p95 latency.

Relationship to the AutoBuild pipeline:

Feature Orchestrator → Waves → Tasks → Player-Coach Turns → LLM Calls + Tool Executions
       │                  │        │            │
  wave.completed    task.started  task.completed/failed   llm.call + tool.exec

Every layer of the pipeline emits events. The instrumentation is always-on JSONL — there is zero runtime cost when the data is not being analysed. Events are written append-only under an async lock and never block the LLM critical path.

Architecture¶

Event Emission Flow¶

┌─────────────────────────────────────────────────────────────────────┐
│                        EVENT SOURCES                                │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  AutoBuildOrchestrator ──► task.started / task.completed / task.failed
│  AgentInvoker ──────────► llm.call / tool.exec                     │
│  FeatureOrchestrator ───► wave.completed                           │
│  GraphitiContextLoader ─► graphiti.query                           │
│                                                                     │
├─────────────────────────────────────────────────────────────────────┤
│                        MIDDLEWARE                                    │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  tool.exec events ──► SecretRedactor ──► redacted tool.exec        │
│  (cmd, stdout_tail, stderr_tail fields are scrubbed)               │
│                                                                     │
├─────────────────────────────────────────────────────────────────────┤
│                        EVENT EMITTER (Protocol)                     │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  EventEmitter Protocol:                                             │
│    async emit(event: BaseEvent) -> None                            │
│    async flush() -> None                                           │
│    async close() -> None                                           │
│                                                                     │
├─────────────────────────────────────────────────────────────────────┤
│                        BACKENDS                                     │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  CompositeBackend (fan-out)                                         │
│    ├── JSONLFileBackend (always-on) → events.jsonl                 │
│    └── NATSBackend (optional) → NATS subject                      │
│                                                                     │
└─────────────────────────────────────────────────────────────────────┘

Backend Options¶

Backend	Purpose	Always On	Notes
JSONLFileBackend	Local JSONL persistence	Yes	One JSON object per line, async-lock protected
NATSBackend	Async publish to NATS	No	Logs warning on failure, never raises
CompositeBackend	Fan-out to multiple backends	—	If one backend fails, others still receive the event
NullEmitter	Testing	—	Optionally captures events in `.events` list

Protocol-Based Injection¶

All orchestrators accept an EventEmitter via constructor injection. In production, a CompositeBackend wrapping JSONLFileBackend (+ optionally NATSBackend) is injected. In tests, NullEmitter(capture=True) is injected for assertion-based testing without file I/O.

Event Types Reference¶

All events extend BaseEvent which provides these common fields:

Field	Type	Description
`run_id`	`str`	Unique identifier for the AutoBuild run
`feature_id`	`str?`	Optional feature identifier
`task_id`	`str`	Task identifier (e.g., `TASK-001`)
`agent_role`	`AgentRole`	`player`, `coach`, `resolver`, or `router`
`attempt`	`int`	1-indexed attempt number
`timestamp`	`str`	ISO 8601 formatted timestamp
`schema_version`	`str`	Default `1.0.0` for forward compatibility

Controlled Vocabularies¶

AgentRole: player | coach | resolver | router

PromptProfile: digest_only | digest+graphiti | digest+rules_bundle | digest+graphiti+rules_bundle

LLMProvider: anthropic | openai | local-vllm

LLMCallStatus: ok | error

LLMCallErrorType: rate_limited | timeout | tool_error | other

GraphitiQueryType: context_loader | nearest_neighbours | adr_lookup

GraphitiStatus: ok | error

llm.call¶

Emitted for every LLM API call made by the Player or Coach.

Field	Type	Description
`provider`	`LLMProvider`	`anthropic`, `openai`, or `local-vllm`
`model`	`str`	Model identifier (e.g., `claude-sonnet-4-20250514`)
`input_tokens`	`int`	Number of input tokens
`output_tokens`	`int`	Number of output tokens
`latency_ms`	`float`	Total call latency in milliseconds
`ttft_ms`	`float?`	Time-to-first-token (streaming only)
`prefix_cache_hit`	`bool?`	Whether vLLM prefix cache was hit
`prefix_cache_estimated`	`bool`	Whether cache hit was estimated
`context_bytes`	`int?`	Context size in bytes
`prompt_profile`	`PromptProfile`	Which prompt assembly strategy was used
`status`	`LLMCallStatus`	`ok` or `error`
`error_type`	`LLMCallErrorType?`	Error classification when status is `error`

Example:

{
  "run_id": "run-a1b2c3d4",
  "feature_id": "FEAT-INST",
  "task_id": "TASK-INST-005b",
  "agent_role": "player",
  "attempt": 1,
  "timestamp": "2026-03-08T10:15:30.123Z",
  "schema_version": "1.0.0",
  "provider": "anthropic",
  "model": "claude-sonnet-4-20250514",
  "input_tokens": 12500,
  "output_tokens": 3200,
  "latency_ms": 8450.2,
  "ttft_ms": 1230.5,
  "prefix_cache_hit": null,
  "prefix_cache_estimated": false,
  "context_bytes": 48000,
  "prompt_profile": "digest+rules_bundle",
  "status": "ok",
  "error_type": null
}

tool.exec¶

Emitted for every tool invocation (Bash, Read, Write, Edit, etc.). Fields are redacted for security.

Field	Type	Description
`tool_name`	`str`	Sanitised tool name
`cmd`	`str`	Command string (secrets redacted)
`exit_code`	`int`	Process exit code
`latency_ms`	`float`	Execution latency in milliseconds
`stdout_tail`	`str`	Truncated tail of stdout (secrets redacted)
`stderr_tail`	`str`	Truncated tail of stderr (secrets redacted)

Example:

{
  "run_id": "run-a1b2c3d4",
  "feature_id": null,
  "task_id": "TASK-INST-005b",
  "agent_role": "player",
  "attempt": 1,
  "timestamp": "2026-03-08T10:16:02.456Z",
  "schema_version": "1.0.0",
  "tool_name": "Bash",
  "cmd": "pytest tests/orchestrator/instrumentation/ -v --tb=short",
  "exit_code": 0,
  "latency_ms": 3200.1,
  "stdout_tail": "12 passed in 2.8s",
  "stderr_tail": ""
}

task.started¶

Emitted when an AutoBuild task begins execution.

No additional fields beyond BaseEvent.

Example:

{
  "run_id": "run-a1b2c3d4",
  "feature_id": null,
  "task_id": "TASK-INST-005b",
  "agent_role": "player",
  "attempt": 1,
  "timestamp": "2026-03-08T10:15:00.000Z",
  "schema_version": "1.0.0"
}

task.completed¶

Emitted when a task finishes successfully.

Field	Type	Description
`turn_count`	`int`	Number of player-coach turns taken
`diff_stats`	`str`	Summary of code changes (e.g., `+50 -10`)
`verification_status`	`str`	Coach verification outcome
`prompt_profile`	`PromptProfile`	Profile used for the task

Example:

{
  "run_id": "run-a1b2c3d4",
  "feature_id": "FEAT-INST",
  "task_id": "TASK-INST-005b",
  "agent_role": "player",
  "attempt": 1,
  "timestamp": "2026-03-08T10:25:00.000Z",
  "schema_version": "1.0.0",
  "turn_count": 3,
  "diff_stats": "+180 -12",
  "verification_status": "pass",
  "prompt_profile": "digest+rules_bundle"
}

task.failed¶

Emitted when a task fails after exhausting retries.

Field	Type	Description
`failure_category`	`FailureCategory`	Categorised reason for failure

Example:

{
  "run_id": "run-a1b2c3d4",
  "feature_id": "FEAT-INST",
  "task_id": "TASK-INST-005b",
  "agent_role": "player",
  "attempt": 3,
  "timestamp": "2026-03-08T10:35:00.000Z",
  "schema_version": "1.0.0",
  "failure_category": "test_failure"
}

wave.completed¶

Emitted after each parallel wave in a feature build completes.

Field	Type	Description
`wave_id`	`str`	Unique identifier for the wave
`worker_count`	`int`	Number of concurrent workers
`queue_depth_start`	`int`	Queue depth at wave start
`queue_depth_end`	`int`	Queue depth at wave end
`tasks_completed`	`int`	Number of completed tasks
`task_failures`	`int`	Number of failed tasks
`rate_limit_count`	`int`	Number of rate-limit events
`p95_task_latency_ms`	`float?`	95th percentile task latency

Example:

{
  "run_id": "run-a1b2c3d4",
  "feature_id": "FEAT-INST",
  "task_id": "wave-2",
  "agent_role": "router",
  "attempt": 1,
  "timestamp": "2026-03-08T10:40:00.000Z",
  "schema_version": "1.0.0",
  "wave_id": "wave-2",
  "worker_count": 3,
  "queue_depth_start": 5,
  "queue_depth_end": 2,
  "tasks_completed": 3,
  "task_failures": 0,
  "rate_limit_count": 0,
  "p95_task_latency_ms": 45000.0
}

graphiti.query¶

Emitted for each Graphiti knowledge graph query.

Field	Type	Description
`query_type`	`GraphitiQueryType`	`context_loader`, `nearest_neighbours`, or `adr_lookup`
`items_returned`	`int`	Number of items returned
`tokens_injected`	`int`	Number of tokens injected into context
`latency_ms`	`float`	Query latency in milliseconds
`status`	`GraphitiStatus`	`ok` or `error`

Example:

{
  "run_id": "run-a1b2c3d4",
  "task_id": "TASK-INST-005b",
  "agent_role": "player",
  "attempt": 1,
  "timestamp": "2026-03-08T10:15:05.000Z",
  "schema_version": "1.0.0",
  "query_type": "context_loader",
  "items_returned": 4,
  "tokens_injected": 2800,
  "latency_ms": 320.5,
  "status": "ok"
}

File Locations¶

Resource	Path
Events file	`.guardkit/autobuild/{task_id}/events.jsonl`
Player reports	`.guardkit/autobuild/{task_id}/player_turn_{turn}.json`
Coach decisions	`.guardkit/autobuild/{task_id}/coach_turn_{turn}.json`
Task-work results	`.guardkit/autobuild/{task_id}/task_work_results.json`
Digest files	`.guardkit/digests/{role}.md` (player, coach, resolver, router)
Schemas module	`guardkit/orchestrator/instrumentation/schemas.py`
Emitter module	`guardkit/orchestrator/instrumentation/emitter.py`
Redaction module	`guardkit/orchestrator/instrumentation/redaction.py`
Digests module	`guardkit/orchestrator/instrumentation/digests.py`
Prompt profiles	`guardkit/orchestrator/instrumentation/prompt_profile.py`
Concurrency	`guardkit/orchestrator/instrumentation/concurrency.py`
LLM helpers	`guardkit/orchestrator/instrumentation/llm_instrumentation.py`
Template pattern loader	`guardkit/knowledge/template_pattern_loader.py`
Template resolver	`guardkit/templates/resolver.py`
Context loader (pattern wiring)	`guardkit/knowledge/autobuild_context_loader.py`
Tests (instrumentation)	`tests/orchestrator/instrumentation/`
Tests (template patterns)	`tests/unit/test_template_pattern_loader.py`

Artifact Archival and Durable Storage¶

Local Archive¶

All AutoBuild run artifacts are automatically archived to a durable location outside the repository working tree before worktree cleanup. This ensures artifacts survive: - git worktree remove --force - git clean -fdx - Repository deletion - Worktree prune operations

Default archive location:

~/.guardkit/archive/<repo-name>/<feature-or-task-id>/

Custom archive root: Set the GUARDKIT_ARCHIVE_ROOT environment variable:

export GUARDKIT_ARCHIVE_ROOT=/path/to/custom/archive
guardkit autobuild feature FEAT-ABC

What gets archived: - All task directories: .guardkit/autobuild/<task_id>/ - player_turn_N.json - Player implementation reports - coach_turn_N.json - Coach validation reports
- task_work_results.json - Task execution results - design_results.json - Design phase outputs - specialist_results.json - Test orchestrator / code reviewer results - sdk_debug/turn_N/ - SDK debug artifacts - _rollback_archive/ - Checkpoint rollback history - state_transitions.json - Task state transitions - Feature-level files: - baseline.json - Wave-0 baseline probe output (L12) - events.jsonl - Full event stream - Both feature-level and task-mode events.jsonl files

Archive timing: - Primary (cleanup hook): Archives at worktree removal (D-OBS-1 decision) - Incremental (task finalize): Archives after each task completes - Crash between finalize and cleanup loses at most the in-flight task - "Belt and braces" redundancy

Important: The local archive root (~/.guardkit/archive/) is one copy on one machine until the NAS rsync runs (see below). It is gitignored local disk storage, not the final durable home.

NAS Durable Home (D-OBS-4)¶

The archive root is node-local (self-contained agents rule). For multi-machine durable storage, rsync archived runs to the NAS after completion.

NAS destination:

whitestocks:~/factory-corpora/<feature-or-task-id>/

NAS user home: /var/services/homes/RichardWoollcott/
(Regular users cannot mkdir at the /volume1 root)

rsync command:

# After a feature run completes
rsync -avz ~/.guardkit/archive/<repo-name>/<feature-id>/ \
  whitestocks:~/factory-corpora/<feature-id>/

# For task-mode runs
rsync -avz ~/.guardkit/archive/<repo-name>/<task-id>/ \
  whitestocks:~/factory-corpora/<task-id>/

Recommended cadence: - After each feature completion (via /feature-complete) - Daily for long-running feature development - Before machine shutdown or cleanup

Automation option: Add to your shell profile or cron:

# ~/.bashrc or ~/.zshrc
alias guardkit-archive-sync='rsync -avz ~/.guardkit/archive/ whitestocks:~/factory-corpora/'

# Run after each feature
guardkit autobuild feature FEAT-ABC && guardkit-archive-sync

Verifying Archive Integrity¶

# Check local archive exists
ls -lh ~/.guardkit/archive/<repo-name>/<feature-id>/

# Count archived turn files
find ~/.guardkit/archive/<repo-name>/<feature-id>/ -name "*turn_*.json" | wc -l

# Verify baseline.json is present (AC-3)
ls ~/.guardkit/archive/<repo-name>/<feature-id>/baseline.json

# Check NAS copy
ssh whitestocks "ls -lh ~/factory-corpora/<feature-id>/"

Archive Failure Handling¶

Archive operations are fail-open (AC-4): - Archive failure logs a WARNING with lost paths - Archive failure does NOT block worktree cleanup - An absent archive is surfaced, never silent

Check logs for warnings:

grep "Failed to archive" <autobuild-log-file>

How to Use¶

Viewing Events¶

# Pretty-print all events for a task
cat .guardkit/autobuild/TASK-001/events.jsonl | jq .

# Count events by type
jq -r '.event_type' .guardkit/autobuild/TASK-001/events.jsonl | sort | uniq -c | sort -rn

Filtering by Event Type¶

# All LLM calls
jq 'select(.event_type == "llm.call")' .guardkit/autobuild/TASK-001/events.jsonl

# All tool executions
jq 'select(.event_type == "tool.exec")' .guardkit/autobuild/TASK-001/events.jsonl

# All failures
jq 'select(.event_type == "task.failed")' .guardkit/autobuild/TASK-001/events.jsonl

Token Usage Summary¶

# Token counts per LLM call
jq 'select(.event_type == "llm.call") | {model, input_tokens, output_tokens, latency_ms}' \
  .guardkit/autobuild/TASK-001/events.jsonl

# Total tokens for a task
jq -s '[.[] | select(.event_type == "llm.call")] |
  {total_input: (map(.input_tokens) | add),
   total_output: (map(.output_tokens) | add),
   call_count: length}' \
  .guardkit/autobuild/TASK-001/events.jsonl

Comparing Prompt Profiles¶

# Group LLM calls by prompt profile
jq 'select(.event_type == "llm.call") | {prompt_profile, input_tokens, latency_ms}' \
  .guardkit/autobuild/TASK-001/events.jsonl

# Average tokens by profile (for A/B comparison)
jq -s '[.[] | select(.event_type == "llm.call")] | group_by(.prompt_profile) | map({
  profile: .[0].prompt_profile,
  avg_input: (map(.input_tokens) | add / length),
  avg_latency: (map(.latency_ms) | add / length),
  count: length
})' .guardkit/autobuild/TASK-001/events.jsonl

Identifying Slow Tasks¶

# LLM calls sorted by latency (slowest first)
jq -s 'map(select(.event_type == "llm.call")) | sort_by(-.latency_ms) | .[:5] |
  .[] | {task_id, model, latency_ms, input_tokens}' \
  .guardkit/autobuild/TASK-001/events.jsonl

# Tool executions sorted by latency
jq -s 'map(select(.event_type == "tool.exec")) | sort_by(-.latency_ms) | .[:5] |
  .[] | {tool_name, cmd: .cmd[:80], latency_ms, exit_code}' \
  .guardkit/autobuild/TASK-001/events.jsonl

Heartbeat Labels¶

Heartbeat log lines emitted by async_heartbeat ([<task_id>] <phase> in progress... (<N>s elapsed)) use distinct phase labels so operators can disambiguate which agent is running:

task-work implementation — the actual task-work Player invocation (the real implementation work).
Player invocation / Coach invocation — direct _invoke_with_role calls without a delegation wrapper (legacy call sites).
specialist:<name> invocation — orchestrator-driven specialists run via run_specialist (specialist:test-orchestrator invocation for Phase 4, specialist:code-reviewer invocation for Phase 5). These run as agent_type=player/coach under the hood but surface a distinct label so they aren't conflated with the actual Player. (TASK-ABSR-DIAG.)

Failure Analysis¶

# All failures grouped by category
jq 'select(.event_type == "task.failed") | {task_id, failure_category, attempt}' \
  .guardkit/autobuild/TASK-001/events.jsonl

# Errors in LLM calls
jq 'select(.event_type == "llm.call" and .status == "error") |
  {task_id, error_type, model, latency_ms}' \
  .guardkit/autobuild/TASK-001/events.jsonl

Prompt Profiles and Digests¶

What Prompt Profiles Are¶

A prompt profile describes which context sources are assembled into the system prompt for each agent invocation. The prompt_profile field on llm.call and task.completed events enables A/B comparison of different prompt strategies.

Profile	Content	Tokens (typical)
`digest_only`	Role-specific digest only	300-600
`digest+graphiti`	Digest + Graphiti knowledge context	600-1200
`digest+rules_bundle`	Digest + full rules bundle	1500-3000
`digest+graphiti+rules_bundle`	All sources combined	2000-4000

Current default: digest+rules_bundle (Phase 1 baseline).

Where Digest Files Live¶

Digest files are role-specific condensed instructions at .guardkit/digests/:

File	Role	Purpose
`player.md`	Player	Implementation rules, output contract, quality expectations
`coach.md`	Coach	Validation rules, output contract, anti-stub enforcement
`resolver.md`	Resolver	Conflict resolution rules
`router.md`	Router	Task routing and wave management rules

Token Budget¶

Target range: 300-600 tokens per digest
Hard limit: 700 tokens (enforced by DigestValidator)
Token counting: Uses tiktoken (cl100k_base encoding) with word-based fallback (~0.75 tokens per word)

Validating Digests¶

from guardkit.orchestrator.instrumentation.digests import DigestValidator
from pathlib import Path

validator = DigestValidator(digest_dir=Path(".guardkit/digests"))

# Validate a single role
result = validator.validate("player")
print(f"Tokens: {result.token_count}, Warning: {result.warning}")

# Validate all roles
results = validator.validate_all()
for r in results:
    print(f"{r.role}: {r.token_count} tokens")

Prompt Profile Migration Phases¶

Phase	Profile	Description
Phase 1 (baseline)	`digest+rules_bundle`	Digest alongside full rules bundle; measures baseline token usage
Phase 2	`digest_only`	Digest only; measures token savings and quality delta
Phase 3	`digest+graphiti`	Digest with Graphiti context; measures knowledge-enhanced quality
Phase 4	`digest+graphiti+rules_bundle`	All sources; measures combined effectiveness

Compare phases by filtering events on prompt_profile and comparing token counts, latency, and task success rates.

Template Pattern Context¶

Overview¶

Template pattern context injects stack-specific .template files into the Player's prompt at build time, giving the AI canonical code shapes to follow when generating code. This was implemented in FEAT-TPL-PLAYER (tasks TASK-TPL-001 through TASK-TPL-004).

Why template patterns exist:

Consistency — The Player sees the same patterns the template author intended, producing code that matches the project's established architecture.
Reduced hallucination — Instead of inferring patterns from agent prose descriptions, the Player reads actual parameterised code templates.
Stack-specific guidance — Each builtin template ships its own .template files (FastAPI routers, .NET endpoints, React components, etc.). Only relevant files are loaded per task.

Data Flow¶

guardkit init <template>
    │
    ├── Copies .claude/ config layer → project
    └── Records template name in .claude/manifest.json
        │
        ... (development happens) ...
        │
/feature-build TASK-XXX
    │
    ▼
AutoBuildContextLoader._append_template_patterns()
    │
    ├── 1. Read .claude/manifest.json → extract `name` field
    │
    ├── 2. resolve_template_source_dir(name)
    │       → installer/core/templates/{name}/
    │       → fallback: ~/.guardkit/templates/{name}/
    │
    ├── 3. load_template_patterns(manifest_path)
    │       → enumerate templates/*.template files
    │
    ├── 4. select_patterns(context, tech_stack, file_path_hints)
    │       → file-path hint matching (priority 1)
    │       → tech-stack keyword fallback (priority 2)
    │       → alphabetical fallback (priority 3)
    │       → cap: max 5 files, ~3000 tokens
    │
    ├── 5. format_pattern_block(context, file_contents)
    │       → markdown block with fenced code blocks
    │
    └── 6. Append to AutoBuildContextResult.prompt_text

Key Components¶

Component	File Path	Purpose
Template pattern loader	`guardkit/knowledge/template_pattern_loader.py`	Reads manifest, resolves template dir, enumerates `.template` files
Template resolver	`guardkit/templates/resolver.py`	Resolves template source directory from name
Context integration	`guardkit/knowledge/autobuild_context_loader.py`	`_append_template_patterns()` wires loader into Player context
Tests	`tests/unit/test_template_pattern_loader.py`	Unit tests for loader, selector, and formatter

Manifest Requirement¶

The .claude/manifest.json file must contain a name field matching a known template:

{
  "name": "fastapi-python",
  "version": "1.0.0"
}

This is written automatically by guardkit init <template>. Projects without a manifest (or with a missing/invalid name field) degrade gracefully — no patterns are loaded, no errors are raised, and the Player proceeds without pattern context.

Pattern Selection Rules¶

Selection follows a priority cascade:

File-path hints — If the task touches files in app/api/, match template files under an api/ subdirectory.
Tech-stack keywords — If no file-path matches, use the tech stack (e.g., "python" → look for api/, core/, config/, models/ subdirectories).
Alphabetical fallback — If nothing else matches, take the first 3 files alphabetically.
Budget enforcement — Maximum 5 files, approximately 3000 tokens. Files that would exceed the budget are skipped with a warning.

Graceful Degradation¶

The template pattern pipeline never raises exceptions. Each failure point produces a warning and returns empty context:

Condition	Behaviour
No `worktree_path` configured	Skip silently (debug log)
`.claude/manifest.json` missing	Skip silently (debug log)
`name` field missing or invalid	Warning logged, empty context
Template source dir not found	Warning logged, empty context
No `templates/` subdirectory	Warning logged, empty context
No files match selection criteria	Skip silently (debug log)
File read error	Individual file skipped, others still loaded

Logging¶

Template pattern loading emits structured log messages at info and warning levels:

[TemplatePattern] Appended pattern block: 3 files, ~750 tokens (api/router.py.template, models/base.py.template, crud/crud_base.py.template)
[TemplatePattern] Skipped db/session.py.template: adding 400 tokens would exceed budget (2800/3000)

Token Impact¶

Typical token costs by template type:

Template	Files Available	Files Selected (typical)	Token Cost
fastapi-python	8-12	3-5	~500-800
dotnet-railway-fastendpoints	18-22	3-5	~600-1000
react-typescript	6-10	3-5	~400-700
python-library	4-6	3-4	~300-500
No manifest / unknown template	—	0	0

The ~500-1000 token overhead is comparable to a single Graphiti context block and is well within the Player's context budget.

Relationship to Other Context Sources¶

Template patterns are additive — they do not replace any existing context source:

Player Context Assembly:
  ├── Graphiti knowledge context (similar outcomes, patterns, ADRs)
  ├── Role constraints and quality gates
  ├── Turn states (cross-turn learning)
  ├── Implementation modes
  ├── Template pattern context  ◄── NEW (FEAT-TPL-PLAYER)
  └── Task description and acceptance criteria

The pattern block is appended to prompt_text after all other context sources. It appears in the Player's prompt as a "## Stack Pattern Reference" section.

Adaptive Concurrency¶

The ConcurrencyController uses wave.completed events to automatically adjust the number of parallel workers during feature builds.

How It Works¶

The FeatureOrchestrator emits a wave.completed event after each wave.
The ConcurrencyController.on_wave_completed() method evaluates the event.
A ConcurrencyDecision is returned with action (maintain, reduce, increase) and new_workers.

Policy Rules¶

Rules are evaluated in order. First match wins.

Priority	Trigger	Action	Detail
1	`rate_limit_count > 0`	Reduce by 50%	`new = max(1, current // 2)`
2	p95 latency > threshold	Reduce by 50%	Threshold = `baseline * (1 + p95_threshold_pct / 100)`
3	Stable for N minutes + reduced	Increase by +1	`new = min(current + 1, initial)`
4	Default	Maintain	No change needed

Configuration¶

from guardkit.orchestrator.instrumentation.concurrency import ConcurrencyController

controller = ConcurrencyController(
    initial_workers=4,          # Starting (and maximum) worker count
    p95_threshold_pct=100.0,    # 100% above baseline triggers reduction
    stability_minutes=5.0       # Minutes of stability before recovery
)

Relevance to Local/vLLM Backends¶

On local inference backends, GPU contention is the primary bottleneck. The adaptive concurrency system is particularly valuable here because:

Rate limits manifest differently — local vLLM servers queue requests rather than returning 429s, causing p95 latency spikes instead.
Recovery is safe — the +1 gradual recovery avoids re-saturating the GPU.
Default max_parallel=1 — for local backends (per TASK-VPT-001), the controller starts conservative and adapts.

vLLM / Local Backend Specifics¶

Provider Detection¶

The detect_provider() function identifies the LLM backend from the base URL:

Base URL Pattern	Detected Provider
`None` or empty	`anthropic`
Contains `api.anthropic.com`	`anthropic`
Contains `openai`	`openai`
Contains `localhost` or `vllm`	`local-vllm`

Prefix Cache Hit Detection¶

For vLLM backends, the x-vllm-prefix-cache-hit response header is inspected:

Header value "true" → prefix_cache_hit=True
Header value "false" → prefix_cache_hit=False
Header absent → prefix_cache_hit=None

The prefix_cache_estimated field is always False for direct header detection.

Key Metrics to Watch¶

Metric	Field	Why It Matters
Time to first token	`ttft_ms`	Measures prefill latency; high values indicate context too large for GPU
Total latency	`latency_ms`	End-to-end call time including generation
Input tokens	`input_tokens`	Prefill cost; digest-only profiles reduce this significantly
Prefix cache hit	`prefix_cache_hit`	Cache hits reduce prefill time; monitor hit rate

vLLM Defaults (from TASK-VPT-001)¶

Setting	Value	Reason
`max_parallel`	1	Prevents GPU contention on single-GPU setups
`timeout_multiplier`	3.0	Local inference is slower than API calls

jq Queries for vLLM Analysis¶

# All local-vllm calls with cache status
jq 'select(.event_type == "llm.call" and .provider == "local-vllm") |
  {model, input_tokens, ttft_ms, latency_ms, prefix_cache_hit}' \
  .guardkit/autobuild/TASK-001/events.jsonl

# Average TTFT for cached vs uncached
jq -s 'map(select(.event_type == "llm.call" and .provider == "local-vllm")) |
  group_by(.prefix_cache_hit) | map({
    cache_hit: .[0].prefix_cache_hit,
    avg_ttft: (map(.ttft_ms // 0) | add / length),
    count: length
  })' .guardkit/autobuild/TASK-001/events.jsonl

See also: Local Backend AutoBuild Guide for vLLM server setup and tuning.

Secret Redaction¶

What Is Redacted Automatically¶

The SecretRedactor applies pattern-based redaction to tool.exec events only. The following patterns are scrubbed and replaced with [REDACTED]:

Pattern	What It Catches
`sk-[A-Za-z0-9_-]{10,}`	OpenAI-style secret keys
`AKIA[A-Z0-9]{12,}`	AWS access key IDs
`gh[ps]_[A-Za-z0-9]{10,}`	GitHub tokens (personal + server)
`bearer [token]` (case-insensitive)	Bearer authentication tokens
`PASSWORD=...`, `PASS=...`, `SECRET=...`	Environment variable secrets
`token=...`, `api_key=...`	Generic token/key parameters
`://user:pass@host`	URL-embedded credentials

Redacted Fields¶

Only tool.exec events are redacted. The affected fields are:

cmd — the command string
stdout_tail — truncated stdout output
stderr_tail — truncated stderr output

Additionally, tool_name is sanitised to remove shell metacharacters (;, |, &, $, `, >, <, (, )).

Adding Custom Redaction Patterns¶

from guardkit.orchestrator.instrumentation.redaction import SecretRedactor

custom_redactor = SecretRedactor(patterns=[
    r"sk-[A-Za-z0-9_-]{10,}",       # Keep defaults
    r"AKIA[A-Z0-9]{12,}",
    r"my-custom-prefix-[A-Za-z0-9]+", # Add your own
])

Pass the custom redactor to redact_tool_exec_event() to apply it.

Never Run Agent Loops With Live Credentials in the Ambient Environment¶

Redaction and publication-boundary scrubbing (TASK-AB-SECRETSCRUB01, guardkit/lib/secret_scrub.py) are the LAST lines of defence, not the first. The ABL-001 run-3 leak (2026-07-04) happened because the loop's ambient environment carried a live Postgres DSN: a non-hermetic test printed it in an expected-vs-actual diff, and that output travelled the standard publication path into a public commit.

The operational rule (the P4 env contract): agent loops run with fixture credentials only — localhost/127.0.0.1 DSNs are the documented, legitimate pattern. Live store credentials have no business in the environment an autonomous loop inherits; export them only in the shells that genuinely talk to the live store, never in the loop launcher. Pair this with hermetic tests (pin the full env-var surface with monkeypatch.setenv/delenv — see TASK-AB-HERMETICTEST01) so a config test can never read the ambient environment at all.

Bootstrap Hard-Fail Gate (`bootstrap_failure_mode`)¶

When AutoBuild creates a shared worktree it runs an environment bootstrap (detect pyproject.toml / package.json / *.csproj / etc., run the matching install). By default this phase is non-blocking — if every install fails the orchestrator prints a yellow-⚠ line and proceeds into Wave 1 anyway.

For the forge/GB10 case this turned into a foot-gun: forge required Python ≥3.13 but the GB10 host ran 3.12, every pip install -e . failed, and AutoBuild still marched into Wave 1. Review TASK-REV-E4F5 recommendation R4a asked for an opt-in gate that turns this into a loud error instead of a silent warning. TASK-FIX-7A04 implements it.

Configuration¶

Set in .guardkit/config.yaml (feature-level), override per-invocation on the CLI:

# .guardkit/config.yaml
autobuild:
  bootstrap:
    failure_mode: warn   # "warn" (default) or "block"
    optional_stacks: []  # stacks that should NOT count toward the essential-stack check

# CLI override — wins over the yaml value
guardkit autobuild feature FEAT-XXX --bootstrap-failure-mode block

Modes¶

Mode	Behavior when 0/N installs succeed	Behavior when partial success	Behavior when all succeed
`warn` (default)	yellow-⚠ line, continue to Wave 1	yellow-⚠ line, continue	green-✓ line, continue
`block`	raise `FeatureOrchestrationError` before Wave 1	yellow-⚠ line, continue	green-✓ line, continue

block fires only on total-failure (installs_failed == installs_attempted) of at least one essential stack (i.e. a detected stack not listed in optional_stacks). Partial success is never blocking — the orchestrator is still making progress.

When to use `block`¶

Dedicated AutoBuild hosts (e.g. GB10-class boxes running claude-agent-sdk) where a silent-broken environment will just produce Coach-runs-against-wrong-interpreter failures a wave later. The loud error at _bootstrap_environment beats a cryptic pytest failure two turns in.
requires-python constraints that don't match the host (the forge/GB10 case). The hard-fail message carries the requires-python value and the PEP-668 stderr tail, so the fix is usually a one-line host upgrade.
CI / scheduled feature builds where an unattended run with a broken venv is worse than a hard failure that gets picked up by the next agent.

When to stay on `warn`¶

Developer machines where you expect to manually reconcile a partial install (e.g. you're iterating on a new stack and the install genuinely may not work yet).
Features that intentionally span optional stacks. Use optional_stacks: to downgrade specific stacks rather than flipping the whole feature back to warn.

Error message shape¶

On hard-fail the error includes the attempted stacks, the manifest path, requires-python (when declared), a PEP-668 marker (when the install tripped externally-managed-environment), the stderr tail, and a hint:

FeatureOrchestrationError: Bootstrap hard-fail: 0/1 install(s) succeeded for essential stack(s): python.
Manifest: /worktree/pyproject.toml
Manifest requires-python: >=3.13
Detected PEP 668 externally-managed-environment failure.
Install stderr (tail):
error: externally-managed-environment
…
Hint: set `bootstrap_failure_mode: warn` in .guardkit/config.yaml (or pass `--bootstrap-failure-mode warn`) to downgrade this to a non-blocking warning.

`requires-python` pre-check¶

The MacBook Pro jarvis FEAT-J002 run surfaced a neighboring failure mode: pip's error for an interpreter/requires-python mismatch is opaque (Package 'jarvis' requires a different Python: 3.14.2 not in '<3.13,>=3.12') and easy to miss in the install log. To cut that diagnostic round-trip the orchestrator runs a pre-pip requires-python check (amendment from TASK-REV-JMBP Workstream E).

How it works:

For every Python manifest, read requires-python from [project] (PEP 621) or [tool.poetry].python (legacy Poetry).
Compare against the active interpreter's version via packaging.specifiers.SpecifierSet.
If any manifest fails the check, act based on bootstrap_failure_mode:
warn — emit a structured ⚠ Python X does not satisfy requires-python=Y`` line per mismatch, then continue to pip (pip remains authoritative).
block — raise before pip runs, with a multi-line remediation hint that names uv, pyenv, and conda install commands for a compatible minor version.

The pre-check is a silent no-op when:

A manifest declares no requires-python constraint.
The specifier string is malformed (pip stays authoritative).
packaging.specifiers is unavailable at import time.

Example block-mode error:

FeatureOrchestrationError: Bootstrap requires-python mismatch (pre-pip).
Python 3.14.2 does not satisfy requires-python=`<3.13,>=3.12` for /worktree/pyproject.toml.
Install a compatible interpreter with one of:
  • uv python install 3.12
  • pyenv install 3.12 && pyenv local 3.12
  • conda create -n <name> python=3.12 && conda activate <name>
Hint: set `bootstrap_failure_mode: warn` in .guardkit/config.yaml (or pass `--bootstrap-failure-mode warn`) to downgrade this to a non-blocking warning.

Wave 2 task TASK-FIX-7A05 wires the bootstrap venv into the Coach pytest interpreter, closing the rest of the GB10 class-of-defect (venv-was-built-but-Coach-ignored-it).
Review: TASK-REV-E4F5 findings F6 and F7.
Amendment: TASK-REV-JMBP Workstream E (requires-python pre-check).

Coach Independent Test Execution (`coach.test_execution`)¶

The Coach's "trust but verify" independent test run has two execution modes. subprocess is the default (TASK-AB-COACHSUBPROC01, 2026-07-04): a deterministic subprocess.run() of pytest pinned to the bootstrap venv interpreter (_resolve_venv_python) — no LLM in the loop, so it cannot hang and cannot fail on SDK transport.

The former default, sdk (pytest run through the Claude Agent SDK Bash tool for "environment parity"), is now opt-in only. The corpus evidence behind the flip (2026-07-04 retro cross-reference, §5 item 15): across the full 2026 retro history the SDK path failed with an opaque exit-1 "Fatal error in message reader" on essentially 100% of invocations across every repo (jarvis, forge, study-tutor, specialist-agent), machine, and vintage — never root-caused, always masked by the subprocess fallback, so each Coach turn paid one doomed SDK attempt. The SDK path stays in-repo (with its subprocess fallback intact) for the open diagnosis tasks TASK-REV-COSE and TASK-FIX-A7B7.

Configuration¶

Resolution precedence: env > config > default (mirrors the GUARDKIT_PHASE4_TEST_EXECUTION pattern from TASK-AB-PERTASKFG01). An invalid value at either tier is ignored with a WARNING, so the safe default always wins over a typo. The active mode and its provenance (env/config/default) are logged at INFO once per Coach validator init.

# .guardkit/config.yaml
autobuild:
  coach:
    test_execution: subprocess   # "subprocess" (default) or "sdk" (opt-in)

# Env override — wins over the yaml value (per-invocation revert lever)
GUARDKIT_COACH_TEST_EXECUTION=sdk guardkit autobuild feature FEAT-XXX

Permanent rollback is a one-line change to DEFAULT_COACH_TEST_EXECUTION in guardkit/orchestrator/quality_gates/coach_validator.py (mirroring the selector.py::DEFAULT_HARNESS cutover shape).

Notes¶

Even under test_execution: sdk, the SDK path is skipped (subprocess is used) for infrastructure-dependent tasks (TASK-REV-CB30 R5), custom ANTHROPIC_BASE_URL endpoints, and the LangGraph harness (TASK-FIX-COACHTESTTO) — and any SDK error still falls back to subprocess.
Both modes pin pytest to the bootstrap venv interpreter (TASK-FIX-COACHPYENV), so environment parity is preserved by the subprocess default.

Troubleshooting¶

If AutoBuild stalls immediately¶

Symptom: AutoBuild exits or stalls within the first few seconds — no task.started event emitted, or the final-summary classification reads player_invocation_stall rather than a mid-loop coach-feedback stall.

This class-of-defect — Player invocation systematically errored before any work happened, and the orchestrator misnamed the problem at summary-time — has been observed twice:

TASK-REV-8A08 on FEAT-486D / TASK-AD-004 (SDK stream timeout)
TASK-REV-E4F5 on FEAT-FORGE-002 (SDK auth + version skew)

Use this triage table to self-diagnose before opening a review:

Symptom (from summary)	Likely cause	Quick check
`player_invocation_stall` + auth error	Not logged into Claude on this host	`claude` CLI login
`player_invocation_stall` + "Unknown message type"	SDK version skew (e.g. `rate_limit_event`)	`pip show claude-agent-sdk` — compare to working host
`player_invocation_stall` + stream/timeout	Network or endpoint config	`ANTHROPIC_BASE_URL` + `vllm-serve.sh` — see TASK-REV-8A08
`unrecoverable_stall [environment_stall]`	Worktree environment is broken — Player gates pass but independent tests fail with `failure_classification == "infrastructure"` for ≥3 consecutive turns (e.g. interpreter mismatch with `requires-python`)	Read the diagnostic line — it names the bootstrap state, the active interpreter, and the manifest's `requires-python`. Set `bootstrap_failure_mode: block` in `.guardkit/config.yaml` so the next run hard-fails at preflight, then install a compatible interpreter via `uv python install <X>` / `pyenv install <X>` / `conda create -n <name> python=<X>`. See TASK-ABSR-C3D4.

Where the signal lives: player_result.error in the Player turn artefact, and recovery_metadata on synthetic reports. The orchestrator currently captures the signal at the call site but does not consult it at final-summary time; treat the summary's stall category as a starting guess, not an authoritative diagnosis, and open player_result.error first.

For environment_stall, the signal lives in the trailing coach_turn_*.json files: each turn carries validation_results.quality_gates.all_gates_passed == True, validation_results.independent_tests.tests_passed == False, and a test_verification issue with failure_classification == "infrastructure". The summary renderer reads <worktree>/.guardkit/bootstrap_state.json and the worktree's pyproject.toml (via DetectedManifest.get_requires_python()) to enrich the diagnostic; missing or corrupt state files are tolerated. Non-Python worktrees fall through to the generic stall message.

Related fix work: TASK-FIX-7A01 pins claude-agent-sdk to a known-good band and logs the version at startup, which removes the SDK-skew failure mode from this table. Until that lands, the pip show claude-agent-sdk check is the fastest way to confirm or rule out version skew. The companion task TASK-ABSR-A1B2 makes bootstrap_failure_mode: block the smart-default when requires-python is declared, eliminating the environment_stall precondition for new runs.

Environment-class conditional approval (`environment_conditional_approval`)¶

When a user explicitly opts into bootstrap_failure_mode: warn and the bootstrap install silently fails, Coach's independent pytest run will hit ImportError / ModuleNotFoundError even though Player's gates passed inside the (broken) venv. Without intervention this presents as a feedback loop on a purely environmental fault — Player has nothing to fix.

The fifth conditional-approval clause in CoachValidator.validate covers this case. It fires when all of the following are true:

failure_class == "infrastructure" and failure_confidence == "ambiguous" (the _INFRA_AMBIGUOUS patterns: ImportError, ModuleNotFoundError, No module named)
gates_status.all_gates_passed == True (Player did everything right inside the venv it had)
task.requires_infrastructure is empty / unset (the existing requires_infrastructure + Docker unavailable branch already handles the declared-deps case)
<worktree>/.guardkit/bootstrap_state.json exists and reports success: False (read by the _bootstrap_likely_broken helper — missing file or success: true is treated as "unknown / can't prove env failure" and the clause does not fire)

When the clause fires, the CoachValidationResult is tagged with environment_conditional_approval: True (visible in the JSON payload's top-level environment_conditional_approval key, alongside the longer-standing approved_without_independent_tests flag). Logs emit at WARNING level:

Conditional approval for {task_id}: environment-class infrastructure failure
(infrastructure/ambiguous) on a known-broken bootstrap; all Player gates passed.
Marking approved with environment flag.

The clause is deliberately narrow:

A real ImportError on a healthy bootstrap (Player imported a non-existent module) does NOT match — bootstrap_state.json reports success: True, so the helper returns False, so the clause doesn't fire, and the existing feedback path runs as before.
A code defect (failure_class == "code") on a broken bootstrap also doesn't match — only the infrastructure/ambiguous classification triggers the clause.
It pairs with the smart-default bootstrap_failure_mode: block work (TASK-ABSR-A1B2) and the environment_stall sub-type detection (TASK-ABSR-C3D4): when this clause fires, neither stall happens; when the clause doesn't fire (e.g. block mode aborted the run at preflight), neither does the stall.

Origin: belt-and-braces layer for TASK-ABSR-A1B2's smart-default, motivated by the FEAT-J004-702C / TASK-J004-004 stall. See TASK-ABSR-2468.

Environment variable tunables¶

Env var	Default	Purpose
`GUARDKIT_MIN_TURN_BUDGET`	`600`	Overrides `MIN_TURN_BUDGET_SECONDS` (the wall-clock floor required before `_loop_phase` starts a new turn). Lower (e.g. `300`) on tasks where Coach feedback is small and short turn-2/3 retries are still useful; the orchestrator will exit with `timeout_budget_exhausted` only when the remaining budget falls below this floor. Read once at module load. See TASK-ABSR-MTBC.
`GUARDKIT_COACH_GRACE_PERIOD_SECONDS`	`1500`	Overrides `COACH_GRACE_PERIOD_SECONDS` — the budget granted to Coach when Player succeeds and the shared task-level `cancellation_event` is set (i.e. the real task-timeout boundary fired during the Player turn). Default raised from 120 → 1500 in TASK-FIX-SPECCOCH01 (Shape B) to cover empirically-observed Coach turn-1 latency under slow coaches (run-9 of FEAT-AOF measured 944 s under gemma4:26b with `--reasoning off`; the default leaves ~50 % headroom for `--reasoning auto`'s richer reasoning channel). The grace branch is now narrowly scoped to real task-timeout boundaries — Shape A of the same task decoupled specialist-hang detection from this event so a healthy hang in the test-orchestrator no longer cascades into Coach being capped at the grace budget. Also reserved upfront by `_cap_specialist_timeout` so specialists never consume the budget Coach might need. Read once at module load. Tune down (e.g. `300`) on fast coaches to reclaim specialist budget; tune up on very slow coaches. See TASK-FIX-SPECCOCH01.

No Events in File¶

Symptom: .guardkit/autobuild/{task_id}/events.jsonl is empty or missing.

Cause: The emitter is not wired into the orchestrator. This was implemented in TASK-INST-013 (CLI emitter wiring).

Fix: Verify that the AutoBuild CLI creates a JSONLFileBackend and injects it into the orchestrators. Check guardkit/cli/autobuild.py for the emitter setup.

DigestLoadError¶

Symptom: DigestLoadError: Digest file not found for role 'player'

Cause: Digest files are missing from .guardkit/digests/. This was implemented in TASK-INST-014 (digest file creation).

Fix: Ensure all four digest files exist:

ls .guardkit/digests/
# Expected: coach.md  player.md  resolver.md  router.md

Events File Growing Large¶

The events file is append-only with no automatic rotation. For long-running feature builds, the file can grow large.

Safe to delete between runs:

rm .guardkit/autobuild/TASK-001/events.jsonl

A new file will be created on the next run. No data loss occurs for the current run — only historical events are removed.

NATS Backend Warnings¶

Symptom: WARNING: NATS publish failed: ...

Cause: The NATS backend is optional. If the NATS server is unreachable, warnings are logged but events are still persisted to JSONL.

Fix: No action required. JSONL persistence is always-on and unaffected by NATS failures. To silence warnings, remove the NATSBackend from the CompositeBackend configuration.

Events with `status: "error"`¶

Filter for error events to diagnose API issues:

# LLM call errors
jq 'select(.event_type == "llm.call" and .status == "error") |
  {task_id, error_type, model, provider}' \
  .guardkit/autobuild/TASK-001/events.jsonl

# Rate limit events specifically
jq 'select(.event_type == "llm.call" and .error_type == "rate_limited") |
  {timestamp, model, provider}' \
  .guardkit/autobuild/TASK-001/events.jsonl

WS4 Learning Flywheel: Appendix A Conformance (TASK-OBS-396E AC-4)¶

Source: TASK-OBS-396E Change 4, mapping to WS4 Learning Flywheel Appendix A (ai-transition/docs/ws4-learning-flywheel-scope-and-build-plan-2026-07-07.md).

This section documents how GuardKit autobuild capture maps to the Appendix A contract that defines what makes a role session valid teacher data for the learning flywheel. Each field is either captured-with-location or an explicit gap with an owner. A role session that is not captured to this contract is not a flywheel input.

Appendix A Field Map¶

#	Appendix A Field	Where Captured in GuardKit	Notes
1	Player input (full assembled prompt + injected context refs)	`<sdk_debug>/turn_N/prompt.txt` per turn/role	Injected context refs: if fleet-memory retrievals are injected, their IDs+scopes are recorded alongside `prompt.txt` in `memory_context.json`; if none injected on this run, that is a verified N/A (not silence).
2	Raw pre-strip output (+ post-strip)	`<sdk_debug>/turn_N/messages.jsonl` (raw stream); post-strip = player reports in `task_work_results.json` / `player_turn_N.json` (archived by TASK-OBS-80FE)	Raw stream includes all LLM tool-use blocks, text content, and harness events. Post-strip reports are the structured Player/Coach verdicts.
3	Human curation events	N/A in autobuild — no human in the loop	Explicit gap: autobuild is fully autonomous (Player-Coach adversarial loop). Human curation is out of scope. Owner: FEAT-SPL-005 (specialist-agent project) for specialist curation flows (e.g. learning-loop human-in-the-loop specialist refinement).
4	Artifact identity (trained-prompt hash, model/checkpoint id, role.yaml sha)	Model: `events.jsonl` `llm.call` events carry `model` + `provider` (e.g. `claude-sonnet-4-5-20250929` / `anthropic`); Prompt profile: TASK-OBS-9F43 wires `prompt_profile` (trained-prompt-hash analogue); Role: `turn_N/` dir structure encodes role (`player` / `coach` / `coach/test_run`); Agent definition: hash of active agent `.md` or digest file recorded in `agent_identity.json` alongside `prompt.txt`.	Each sub-field is captured or explicit N/A. `prompt_profile` per-call identifies digest-only vs digest+rules_bundle strategies for A/B comparison.
5	Correlation IDs (planning correlation_id, task/feature IDs, spec-id, run_id)	Task/feature IDs: `sdk_debug` dir structure under `.guardkit/autobuild/<TASK-ID>/sdk_debug/`; `events.jsonl` carries `task_id` + `feature_id` per event; Run ID: TASK-OBS-9F43 adds `run_id` join; Planning correlation_id + spec-id: explicit N/A for autobuild — these originate in the WS1/forge planning loop, not autobuild.	Verified N/A, not silence: planning correlation_id and spec-id are forge/specialist-agent concerns. Autobuild tasks have `task_id` as primary correlation. Owner: FEAT-SPL-005 for planning-loop traceability.
6	Memory retrievals actually injected (entry IDs + scopes)	If fleet-memory retrievals are injected into prompts (via `ContextLoader`), record IDs+scopes in `memory_context.json` alongside `prompt.txt`; if no retrievals injected on the autobuild path, state that as verified N/A (not silence).	Autobuild may or may not inject memory context depending on feature. When injected, full IDs+scopes are preserved. When not injected, that is an explicit, documented gap (not inferred from absence).

Capture Activation: Default-On with Structural Flip-Gating¶

Per TASK-OBS-396E Change 1-3a, capture defaults ON in named non-client repos (guardkit, study-tutor, forge, fleet-) with structural flip-gating* — the default-on path activates ONLY when:

Rotation caps resolve to positive values (per-turn: 20MB default, per-task: 200MB default), AND
Keep-out-of-git check passes (git check-ignore confirms sdk_debug paths are ignored)

If either guard fails, capture stays OFF with a WARNING naming the failed prerequisite. Client/FinProxy repos stay opt-in per run via GUARDKIT_AUTOBUILD_PRESERVE_DEBUG=1.

Harness Verification (AC-5): LangGraph Default¶

Capture is verified on the LangGraph harness (default since TASK-HMIG-011). The buffered-yield substrate differs from the streaming SDK harness:

Prompt preservation: Guaranteed on both substrates (happens in agent_invoker before harness selection, agent_invoker.py:3966).
Message stream preservation: On SDK harness, per-event stream is preserved. On LangGraph harness (buffered ainvoke), message-level events may not be available per-event; messages.jsonl captures the final assembled response. If the LangGraph path yields no per-event stream, that is a surfaced, documented gap (absent signal ≠ captured), with prompt.txt capture still guaranteed.

Size-Capped Rotation (AC-2)¶

Per-turn cap on messages.jsonl: truncates at configured byte cap (default 20MB) with explicit [TRUNCATED at <N> bytes] marker. Per-task total cap on sdk_debug/ dir (default 200MB): oldest-turn pruning leaves PRUNED.marker naming what was dropped. Rotation never makes capture silently absent.

Archive Durability (AC-6)¶

Captured dirs survive worktree cleanup via TASK-OBS-80FE archive. Integration test joins the two: autobuild run → guardkit worktree cleanup → archived sdk_debug present under .guardkit/archive/<run_id>/<task_id>/sdk_debug/.

AutoBuild Workflow Guide — Full AutoBuild architecture, Player-Coach loop, and CLI usage
Local Backend AutoBuild Guide — vLLM server setup, model alignment, and performance tuning
CLI vs Claude Code — When to use the CLI vs slash commands
Template Pattern Player Context — Feature spec for template pattern injection (FEAT-TPL-PLAYER)

Pre-merge `task_work_results.json` verification¶

Source: TASK-AB-FIX-INVAB1 AC-013 (review report Appendix C). Cross-link from .claude/rules/autobuild.md.

Before merging an autobuild branch, run this defence-in-depth check to catch the FEAT-6CC5 class of false-positive approval (Player-promised files that don't actually exist on disk). The deterministic Coach gate in coach_validator.py already enforces this on every turn after TASK-AB-FIX-INVAB1, but the standalone script is useful when reviewing historical archives or auditing a turn artefact in isolation.

# Detect promises that name implementation_files inconsistent with the
# Player's own files_created/files_modified lists.
python3 -c '
import json, sys
d = json.load(open(sys.argv[1]))
promised = {f for p in d.get("completion_promises", [])
              for f in (p.get("implementation_files") or [])
              if p.get("status") == "complete"}
actual = set(d.get("files_created", []) + d.get("files_modified", []))
missing = promised - actual
if missing:
    print(f"FALSE-POSITIVE INDICATOR: {sorted(missing)}")
    sys.exit(1)
print("ok")
' .guardkit/autobuild/TASK-XXX/task_work_results.json

Exit code 0 = pass; exit code 1 = at least one promised implementation_file is absent from the Player's own files_created / files_modified lists, indicating either (a) the Player misreported its own changes or (b) the file genuinely does not exist on disk. Either way, do not merge until the discrepancy is investigated.

Architectural-regression detector — confirms CoachVerifier is wired into the deterministic Coach path:

$ grep -c "CoachVerifier\|_verify_player_claims\|HonestyVerification\|honesty_verification" \
    guardkit/orchestrator/quality_gates/coach_validator.py
# Post TASK-AB-FIX-INVAB1: must be ≥ 5 (imports + helpers + dataclass field
# + to_dict mirror). A zero result indicates the architectural regression
# has been re-introduced — see TASK-INV-AB1 review report.

AutoBuild Instrumentation Guide¶

Table of Contents¶

Overview¶

Architecture¶

Event Emission Flow¶

Backend Options¶

Protocol-Based Injection¶

Event Types Reference¶

Controlled Vocabularies¶

llm.call¶

tool.exec¶

task.started¶

task.completed¶

task.failed¶

wave.completed¶

graphiti.query¶

File Locations¶

Artifact Archival and Durable Storage¶

Local Archive¶

NAS Durable Home (D-OBS-4)¶

Verifying Archive Integrity¶

Archive Failure Handling¶

How to Use¶

Viewing Events¶

Filtering by Event Type¶

Token Usage Summary¶

Comparing Prompt Profiles¶

Identifying Slow Tasks¶

Heartbeat Labels¶

Failure Analysis¶

Prompt Profiles and Digests¶

What Prompt Profiles Are¶

Where Digest Files Live¶

Token Budget¶

Validating Digests¶

Prompt Profile Migration Phases¶

Template Pattern Context¶

Overview¶

Data Flow¶

Key Components¶

Manifest Requirement¶

Pattern Selection Rules¶

Graceful Degradation¶

Logging¶

Token Impact¶

Relationship to Other Context Sources¶

Adaptive Concurrency¶

How It Works¶

Policy Rules¶

Configuration¶

Relevance to Local/vLLM Backends¶

vLLM / Local Backend Specifics¶

Provider Detection¶

Prefix Cache Hit Detection¶

Key Metrics to Watch¶

vLLM Defaults (from TASK-VPT-001)¶

jq Queries for vLLM Analysis¶

Secret Redaction¶

What Is Redacted Automatically¶

Redacted Fields¶

Adding Custom Redaction Patterns¶

Never Run Agent Loops With Live Credentials in the Ambient Environment¶

Bootstrap Hard-Fail Gate (bootstrap_failure_mode)¶

Configuration¶

Modes¶

When to use block¶

When to stay on warn¶

Error message shape¶

requires-python pre-check¶

Related work¶

Coach Independent Test Execution (coach.test_execution)¶

Configuration¶

Notes¶

Troubleshooting¶

If AutoBuild stalls immediately¶

Environment-class conditional approval (environment_conditional_approval)¶

Environment variable tunables¶

No Events in File¶

DigestLoadError¶

Events File Growing Large¶

Bootstrap Hard-Fail Gate (`bootstrap_failure_mode`)¶

When to use `block`¶

When to stay on `warn`¶

`requires-python` pre-check¶

Coach Independent Test Execution (`coach.test_execution`)¶

Environment-class conditional approval (`environment_conditional_approval`)¶

Events with `status: "error"`¶

Pre-merge `task_work_results.json` verification¶