vamseeachanta Worker Discovery Protocol
When a worker discovers something important during execution -- a bug, a pattern, a convention, a tool quirk, a performance insight -- that knowledge must not die with the worker's session. This skill defines how workers capture discoveries and how orchestrators propagate them to the shared knowledge base.
Why This Exists
Workers operate with fresh context and encounter the codebase without assumptions. This makes them excellent at discovering:
- Undocumented conventions ("this file uses tabs, not spaces")
- Hidden dependencies ("module A silently imports module B at runtime")
- Tool quirks ("pytest requires --no-header flag for clean output here")
- Bug patterns ("this function fails silently when input is empty")
- Performance insights ("this query takes 30s without the index")
Without a capture protocol, these discoveries vanish when the worker session ends. The next worker (or the same orchestrator in a future session) rediscovers the same things, wasting time and tokens.
Worker Responsibilities
During Execution: Append to Discovery Log
When a worker encounters something noteworthy, it appends to a discovery log file in the planning directory:
REPO_ROOT="$(git rev-parse --show-toplevel)"
DISCOVERY_FILE="$REPO_ROOT/.planning/discoveries/$(date +%Y-%m-%d)-worker.jsonl"
mkdir -p "$(dirname "$DISCOVERY_FILE")"
# Append one JSON line per discovery
echo '{"ts":"'"$(date -Iseconds)"'","issue":"#NNN","category":"bug","summary":"function X fails silently on empty input","detail":"src/module.py:42 returns None instead of raising ValueError","severity":"medium","source":"worker-codex-1"}' >> "$DISCOVERY_FILE"
Discovery Categories
| Category | When to log | Example |
|---|---|---|
bug |
Found a bug not in the issue scope | Silent failure, race condition, edge case |
convention |
Found an undocumented code convention | Naming pattern, import order, config format |
dependency |
Found a hidden or undocumented dependency | Runtime import, implicit file requirement |
quirk |
Found a tool or environment quirk | Flag requirement, version sensitivity |
performance |
Found a performance-relevant insight | Slow query, expensive operation, caching opportunity |
security |
Found a security-relevant issue | Exposed credential path, missing validation |
pattern |
Found a reusable code or workflow pattern | Helper function, test fixture, config template |
At Session End: Summary Block
Before the worker session ends, it writes a summary block at the end of its output or commit message:
## Worker Discoveries
- [bug] `src/parser.py:87` — silent failure on malformed input (not in scope, logged)
- [convention] Tests in this module use `@pytest.fixture(autouse=True)` pattern
- [quirk] `uv run` requires `--no-project` flag when running from subdirectory
Orchestrator Responsibilities
After Worker Returns: Triage Discoveries
The orchestrator reads the worker's discovery log and triages:
| Action | When | How |
|---|---|---|
| Create issue | Bug or security finding outside current scope | gh issue create with discovery details |
| Update skill | Convention or pattern that should be codified | Edit relevant SKILL.md or create new one |
| Update KNOWLEDGE.md | Quirk or insight that affects future sessions | Append to .claude/memory/KNOWLEDGE.md |
| Update memory | Correction to agent behavior or preference | Add to auto-memory via conversation |
| Discard | Already known or one-off observation | No action needed |
Propagation Targets
| Discovery type | Primary target | Secondary target |
|---|---|---|
| Bug | GitHub issue | None (tracked in issue) |
| Convention | Relevant SKILL.md | .claude/rules/ if universal |
| Dependency | Module docstring or README | KNOWLEDGE.md |
| Quirk | KNOWLEDGE.md | .claude/memory/topics/ |
| Performance | KNOWLEDGE.md | GitHub issue if actionable |
| Security | GitHub issue (priority:high) | KNOWLEDGE.md |
| Pattern | New or existing SKILL.md | docs/methodology/ |
Monthly Consolidation
The nightly comprehensive-learning pipeline (scripts/cron/comprehensive-learning-nightly.sh)
processes .planning/discoveries/*.jsonl files automatically. Orchestrators do not
need to manually process old discovery logs.
Integration with Existing Systems
Comprehensive Learning Pipeline
The discovery JSONL format is compatible with the session signals format used by the
comprehensive-learning pipeline. Discovery files in .planning/discoveries/ are
picked up during the nightly knowledge harvesting phase.
Agent Memory Bridge
Cross-agent propagation uses the existing agent-memory-bridge skill. When a
discovery applies to all agents (not just the discovering worker's provider):
- Update
.claude/memory/KNOWLEDGE.md(Claude Code picks this up) - Use
agent-memory-bridgeskill to sync to Hermes, Codex, and Gemini
Extract Learnings to Issues
The extract-learnings-to-issues skill can process discovery logs and create
GitHub issues for actionable findings, preventing duplicate issue creation.
Anti-Patterns
Worker modifies knowledge files directly. Workers should LOG discoveries, not update shared knowledge. The orchestrator triages and routes.
Orchestrator ignores discovery log. If the orchestrator does not triage, discoveries are lost until the nightly pipeline runs (which may not extract the same insights an orchestrator would).
Logging everything. Not every observation is a discovery. Workers should log only things that would save time for future workers or prevent future bugs.
Logging without category. Uncategorized discoveries are harder to triage. Always include the category field.
Quick Reference
Worker (during execution)
1. Encounter something noteworthy
2. Append to .planning/discoveries/YYYY-MM-DD-worker.jsonl
3. Include in session-end summary block
Orchestrator (after worker returns)
1. Read .planning/discoveries/*.jsonl from today
2. Triage each discovery: issue / skill / knowledge / memory / discard
3. Execute the routing action
4. Mark discovery as processed (optional — nightly pipeline handles cleanup)
References
- Comprehensive learning:
.claude/skills/coordination/comprehensive-learning-wrapper/SKILL.md - Agent memory bridge:
.claude/skills/coordination/agent-memory-bridge/SKILL.md - Extract learnings: use
/learn-extendedskill - Orchestrator-worker methodology:
docs/methodology/orchestrator-worker.md - Session governance:
docs/governance/SESSION-GOVERNANCE.md