Self Improvement

Self-Improvement Skill

Logs every correction and failed approach to tasks/lessons.md with structured metadata, escalates recurring patterns into skill amendments via scripts/lesson-updater.sh, and respects the cache-aware mutation rule so amendments don't trash Copilot's prompt cache mid-session. Does NOT delete or rewrite past lessons — history is evidence, not noise.

The memory vault (memory/) extends this with structured, linked knowledge:

• Lessons live in tasks/lessons.md (short-term capture)
• Patterns graduate to memory/patterns/ when they hit 3+ occurrences
• Decisions are recorded in memory/decisions/ for architectural context
• Preferences accumulate in memory/preferences/ from user corrections
• Sessions are summarized in memory/sessions/ with links to all of the above

When to Use

• Session start: Review memory/INDEX.md + tasks/lessons.md before doing anything else
• After any correction: User points out a mistake or a better approach
• After a failed approach: Something you tried didn't work
• Pattern recognition: You notice you've made a similar mistake before
• Skill amendment: A pattern hits 3+ occurrences
• After any architectural decision: Record it in memory/decisions/
• Session end: Write a session summary in memory/sessions/

Prerequisites

• tasks/lessons.md exists (created by scripts/init.sh).
• scripts/lesson-updater.sh available for the pattern-scan + amendment proposal.
• Familiarity with the cache-aware mutation rule (see AGENTS.md → Cache-Aware Mutations).

Before any work begins:

1. Read memory/INDEX.md — understand what knowledge exists
2. Read tasks/lessons.md — check for recent, un-promoted lessons
3. Query relevant context: bash scripts/memory-query.sh --type pattern --tag <current-task-domain>
4. Filter for entries relevant to the current project, language, or task type
5. Internalize active rules — these are your guardrails for this session
6. Note any lessons with high occurrence counts — these are your blind spots

How to Run

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1. At session start: `view tasks/lessons.md` and internalize active rules.
2. On correction: append a structured YAML entry (template in Procedure §2).
3. Periodically: `scripts/lesson-updater.sh` to scan for 3+ recurrences.
4. On amendment: edit the relevant skill; default to deferred invalidation.

Quick Reference

Procedure

Step 1: Session-Start Ritual

Before any work begins, view tasks/lessons.md. Filter mentally for entries relevant to the current project, language, or task type. High occurrences counts are your active blind spots — treat them as guardrails for this session.

Step 2: Capture a Lesson

After any correction or failed approach, append a structured entry to tasks/lessons.md:

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- date: 2026-05-19
  error_type: type-error          # type-error | logic-bug | test-gap | over-engineering | scope-creep | …
  trigger: "Used string where number was expected in API response handler"
  root_cause: "Did not check the API schema before assuming response types"
  fix: "Added type validation at the API boundary"
  rule: "Verify API response types against the schema before using them"
  occurrences: 1
  status: active                  # active | resolved | amended-to-skill
  related_skill: code-review      # optional — which skill should absorb the rule?

Be ruthlessly honest. The only person you're fooling is yourself.

Step 3: Watch for Patterns

When the same rule appears across 2+ entries, increment occurrences on the canonical entry instead of duplicating. At occurrences: 3, escalate.

Step 4: Amend the Relevant Skill

1. Identify the pattern — What's the common thread across occurrences?
2. Draft a rule — Write a concrete, actionable rule
3. Promote to memory vault — Create a file in memory/patterns/ using the template:

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   cp memory/patterns/_template.md memory/patterns/pattern-name.md
   

4. Propose the amendment — Suggest an update to skills.md or copilot-instructions.md
5. Run scripts/lesson-updater.sh — Automated pattern scanning
6. Run scripts/link-index.sh — Update the link graph with new connections
7. Evaluate — After the rule is in place, does the mistake rate drop?
8. Revise or remove — If a rule isn't working, fix it or drop it. Dead rules are noise.

Recording Decisions

When an architectural or design decision is made during a session:

1. Copy the template: cp memory/decisions/_template.md memory/decisions/YYYY-MM-DD-short-name.md
2. Fill in context, decision, alternatives, and consequences
3. Link from the session summary: [decision](../decisions/YYYY-MM-DD-short-name.md)
4. Run scripts/link-index.sh to update backlinks

Recording Preferences

When the user corrects your approach (not a bug, but a style/approach preference):

1. Copy the template: cp memory/preferences/_template.md memory/preferences/preference-name.md
2. Record what they prefer and why
3. Set confidence to low initially — raise to medium after 2nd reinforcement, high after 3rd

Session End Ritual

At the end of every non-trivial session:

1. Create memory/sessions/YYYY-MM-DD-summary.md from template
2. Link to any decisions, patterns, or preferences created/referenced
3. Run scripts/link-index.sh to rebuild the graph
4. Update tasks/lessons.md metrics

Pass --now only when correctness requires immediate effect. The script prints a warning about cache-invalidation cost.

After the amendment lands, set the lesson's status: amended-to-skill.

Step 5: Let the Curator Handle Stale Skills

Skills with created_by: agent and no recent use are auto-archived by scripts/curator.sh to skills/.archive/. Human-authored skills are never touched. Pin a skill to exempt it: bash scripts/curator.sh pin <name>.

Pitfalls

• DO NOT "remember next time." If a lesson isn't in tasks/lessons.md, it doesn't exist.
• DO NOT log without a rule: field. A symptom without a prevention rule is a diary entry.
• DO NOT write hyper-specific rules. "Don't use parseInt on line 47 of auth.js" won't generalize. Extract the principle.
• DO NOT delete old lessons. Set status: resolved instead — history is evidence.
• DO NOT pass --now to lesson-updater.sh casually. It invalidates Copilot's prompt cache and dramatically raises cost.
• DO NOT edit created_by: human skills via the curator. The curator only manages created_by: agent skills.

Verification

Concrete checks that prove the skill is producing intended outcomes. Run at session end (or after any correction) via the powershell tool:

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bash scripts/self-improvement-verify.sh

The script (source: scripts/self-improvement-verify.sh) asserts:

1. tasks/lessons.md exists — required artifact.
2. Every lesson has a rule: field — a lesson without a prevention rule is a diary entry and fails the check.
3. No duplicated active rules — the same rule: across multiple entries means someone cloned instead of incrementing occurrences:.
4. Threshold promotion happened — any entry with occurrences: 3+ must either carry status: amended-to-skill or have a corresponding file under memory/patterns/.
5. Memory graph has zero orphans — runs scripts/link-index.sh and inspects memory/INDEX.md.
6. Today's session summary (if any) links out — at least one link to memory/decisions/, memory/patterns/, or memory/preferences/.

The script exits non-zero on hard failures and prints Summary: pass=… fail=… warn=…. Treat warnings as signal, not noise — investigate before closing the session.

Beyond the script: active rules should reduce repeat errors. If the same error_type + trigger recurs within 5 subsequent sessions after a rule lands, the rule is wrong — rewrite or remove it per Step 4 §8.