/sniper-learn

Manage the SNIPER learning store. Submit new learnings from experience, review existing learnings, or deprecate outdated ones.

Mode Selection

4 lines

--review given?             → Review mode
--deprecate <id> given?     → Deprecate mode
learning text given?        → Submit mode
Nothing given?              → Submit mode (prompt for learning text)

Submit Mode

Submit a new learning from human experience or observation.

Process

1. Capture the learning text from the argument or prompt the user:

   • "What did you learn? Describe the pattern, rule, or insight."

2. Ask clarifying questions (present as multi-select, all optional):

   • Agents: Which agents should see this learning? (default: all)
     • Options: analyst, architect, product-manager, fullstack-dev, backend-dev, frontend-dev, qa-engineer, code-reviewer
   • Phases: Which protocol phases does this apply to? (default: all)
     • Options: discover, plan, solve, implement, review
   • Files: Any specific file patterns? (default: all)
     • Accept glob patterns like src/api/**, *.test.ts

3. Ask for anti-pattern and correction (optional):

   • "Is there a specific anti-pattern to avoid?"
   • "What should be done instead?"

4. Create the learning record:

   22 lines

   id: L-{YYYYMMDD}-{4-char-hex}
   status: active
   confidence: 0.9
   created_at: {ISO 8601}
   updated_at: {ISO 8601}
   source:
     type: human
     detail: "Submitted via /sniper-learn"
   learning: {learning text}
   anti_pattern: {if provided}
   correction: {if provided}
   scope:
     agents: {selected or null}
     phases: {selected or null}
     files: {selected or null}
   applied_in: []
   reinforced_by: []
   contradicted_by: []
   history:
     - timestamp: {ISO 8601}
       event: created
       actor: human

5. Write to .sniper/memory/learnings/{id}.yaml

6. Confirm: "Learning {id} created with confidence 0.9. It will be composed into agent prompts for matching phases/agents."

Review Mode

Review, curate, and manage existing learnings.

Process

1. Spawn the memory-curator agent from .claude/agents/memory-curator.md

   • Pass it the task: "Run full curation — consolidation, contradiction detection, staleness check, spec drift detection, signal migration, and pruning."

2. Present curator summary to the user

3. Show flagged items requiring human decision:

   • Contradictions between high-confidence learnings
   • Stale learnings that might still apply
   • Spec drift detections

4. For each flagged item, ask the user:

   • Keep — maintain current status
   • Deprecate — set status to deprecated
   • Edit — modify the learning text/scope

5. Show final state:

   5 lines

   Active learnings: N
   Validated: N
   Deprecated: N
   Archived: N
   Average confidence: X.XX

Deprecate Mode

Deprecate a specific learning by ID.

Process

1. Read .sniper/memory/learnings/{id}.yaml
2. If not found, report error: "Learning {id} not found."
3. Show the learning to the user for confirmation:
   • Learning text, current confidence, source, created date
4. Confirm: "Deprecate this learning?"
5. Update the learning:
   • status: deprecated
   • confidence: 0.0
   • Add history entry:
     4 lines

     - timestamp: {ISO 8601}
       event: human_invalidated
       actor: human
       detail: "Deprecated via /sniper-learn --deprecate"

6. Confirm: "Learning {id} deprecated. It will no longer be composed into agent prompts."

Rules

• Human-submitted learnings ALWAYS start at confidence 0.9
• ALWAYS write to .sniper/memory/learnings/ — never to signals
• ALWAYS include a history entry for every change
• If .sniper/memory/learnings/ doesn't exist, create it
• If .sniper/memory/signals/ contains files, suggest running --review to migrate them