self-reflection

name	Self Reflection
description	Post-task reflection protocol for extracting lessons, identifying gaps, promoting knowledge, and driving system growth. Turns ephemeral experience into permanent wisdom.
version	1.0.0
triggers	["task completion","session handoff","verification failure (post-mortem)","agent detects recurring friction pattern"]

🪞 Self Reflection — The Growth Engine

"An unreflected session is a wasted session. Friction that isn't captured is friction repeated."

This skill operationalizes the Growth Flywheel from COGNITIVE_TREE.md: ACCEPT → SLAP → TRUST → GROW.

---

Reflection Flowchart

flowchart TD
    TRIGGER["Reflection triggered\n(task done, handoff, failure)"] --> COLLECT["① COLLECT\nGather session data"]
    COLLECT --> CLASSIFY["② CLASSIFY\nSort findings by type"]
    CLASSIFY --> EVALUATE["③ EVALUATE\nApply quality gate"]
    EVALUATE --> RECORD["④ RECORD\nPersist to lesson store"]
    RECORD --> PROMOTE["⑤ PROMOTE\nEscalate if pattern found"]
    PROMOTE --> Q1{"Pattern found\n≥ 3 occurrences?"}
    Q1 -->|"Yes"| RULE["Propose new rule\n(use skill-creator)"]
    Q1 -->|"No"| DONE["✅ Reflection complete"]

---

Phase ①: COLLECT — Gather Session Data

Review the work done and answer these questions:

Question	Evidence Source
What was the task?	Ticket/task description
What did I plan to do?	Implementation plan
What did I actually do?	Git diff, file changes
What went wrong?	Error logs, failed attempts
What went right?	Successful verifications
What surprised me?	Unexpected behaviors, edge cases
What took longer than expected?	Time/effort observations

Do Not Do

• Skip collection because "nothing interesting happened"
• Rely on memory instead of evidence (check actual diffs)
• Collect only successes — failures are the richest learning source

---

Phase ②: CLASSIFY — Sort Findings

Each finding gets classified into exactly one category:

Tag	Meaning	Example
[DEBT]	Technical debt introduced	"Used any type to bypass strict mode"
[NUGGET]	Valuable pattern discovered	"BOM stripping must happen before regex"
[GAP]	Missing capability or documentation	"No guard for YAML syntax validation"
[FRICTION]	Process that slowed work	"Had to read 5 files to understand config flow"
[WIN]	Something that worked well	"Evidence tagging caught a false assumption early"

Do Not Do

• Tag everything as [NUGGET] — be honest about debt and gaps
• Use generic descriptions — be specific about file, line, context
• Skip [FRICTION] items — they are seeds for process improvement

---

Phase ③: EVALUATE — Apply Quality Gate

Every finding must pass the quality gate from rule-lesson-quality:

flowchart TD
    FINDING["Finding"] --> Q1{"Has specific\nscenario?"}
    Q1 -->|"No"| DROP["Drop — too generic"]
    Q1 -->|"Yes"| Q2{"Has wrong\napproach?"}
    Q2 -->|"No"| ENRICH["Enrich with\nwhat was tried"]
    Q2 -->|"Yes"| Q3{"Has correct\napproach?"}
    Q3 -->|"No"| ENRICH
    Q3 -->|"Yes"| PASS["✅ Quality passed"]
    ENRICH --> Q3

Quality Score (required ≥ 3)

Score	Criteria
❌ 0	Generic, no context
❌ 1	Has context but no wrong/correct approach
⚠️ 2	Has approaches but vague
✅ 3	Specific scenario + concrete wrong + correct approaches

Do Not Do

• Record "always test your code" — rejected (score 0)
• Record "the guard failed" without explaining WHY — rejected (score 1)
• Skip the quality gate because "it's obvious" — nothing is obvious

---

Phase ④: RECORD — Persist to Lesson Store

Format each qualified finding as a lesson:

{
  scenario: "Guard hollow-artifact failed on UTF-8 files with BOM",
  wrongApproach: "Applied regex pattern at position 0, BOM shifted content by 3 bytes, false negative",
  correctApproach: "Added BOM detection (0xEF,0xBB,0xBF) and strip step in normalize() before pattern matching",
  insight: "Always normalize text encoding before applying content-matching patterns",
  searchTerms: ["BOM", "UTF-8", "regex", "hollow-artifact", "normalize"],
  tags: ["guard", "encoding", "false-negative"],
  evidenceLevel: "RUNTIME",
  relatedFiles: ["src/guards/hollow-artifact.ts"]
}

Storage Location

Project Has	Store In
lessons.jsonl	Append to file
Memory MCP	Call record_lesson tool
Neither	Create lessons.jsonl at project root

Do Not Do

• Store lessons in private workspace only — they must be git-tracked
• Skip searchTerms — a lesson that can't be recalled doesn't exist
• Use [HYPO] evidence level for verified findings — promote to [RUNTIME]

---

Phase ⑤: PROMOTE — Escalate Patterns

When a pattern appears ≥ 3 times across sessions:

flowchart TD
    PATTERN["Same lesson\nrecorded 3+ times"] --> Q1{"What kind\nof pattern?"}
    Q1 -->|"Constraint\nviolation"| RULE["Promote to RULE\n(.agents/rules/)"]
    Q1 -->|"Repeatable\ncapability"| SKILL["Promote to SKILL\n(.agents/skills/)"]
    Q1 -->|"Multi-step\nprocess"| WORKFLOW["Promote to WORKFLOW\n(.agents/workflows/)"]
    Q1 -->|"Architecture\ninsight"| TREE["Update COGNITIVE_TREE\n(.agents/philosophy/)"]

Promotion Protocol

1. Count occurrences of similar lessons
2. If ≥ 3 → identify the pattern
3. Use skill-creator to create the appropriate artifact
4. Cross-reference the original lessons as evidence

Do Not Do

• Promote after just 1 occurrence — wait for the pattern to confirm
• Create a rule that contradicts an existing one — update the existing rule instead
• Promote without evidence — the lesson chain IS the evidence

---

Reflection Output Template

## Session Reflection — {date}

### Summary
{1-2 sentences on what was accomplished}

### Findings

| # | Tag | Finding | Quality |
|:---|:---|:---|:---:|
| 1 | [NUGGET] | {specific finding} | ✅ 3 |
| 2 | [DEBT] | {specific debt} | ✅ 3 |
| 3 | [FRICTION] | {specific friction} | ✅ 3 |

### Lessons Recorded
- {lesson 1 — scenario + insight}
- {lesson 2 — scenario + insight}

### Promotion Candidates
- {pattern seen N times — candidate for rule/skill/workflow}

### HANDOFF (if session ending)
- Current state: {what's done}
- Next action: {what should happen next}
- Key files: {relevant paths}