self-improvement

Self-Improvement Skill

Log learnings and errors to markdown files for continuous improvement. Agents can later process these into fixes, and important learnings get promoted to workspace memory. This version of the skill is built for OpenClaw only — for other agents, see the original multi-agent version at https://github.com/pskoett/pskoett-ai-skills.

First-Use Initialisation

Before logging anything, ensure the .learnings/ directory and files exist in the project or workspace root. If any are missing, create them:

mkdir -p .learnings
[ -f .learnings/LEARNINGS.md ] || printf "# Learnings\n\nCorrections, insights, and knowledge gaps captured during development.\n\n**Categories**: correction | insight | knowledge_gap | best_practice\n\n---\n" > .learnings/LEARNINGS.md
[ -f .learnings/ERRORS.md ] || printf

Never overwrite existing files. This is a no-op if .learnings/ is already initialised.

Do not log secrets, tokens, private keys, environment variables, or full source/config files unless the user explicitly asks for that level of detail. Prefer short summaries or redacted excerpts over raw command output or full transcripts.

If you want automatic reminders and session-end error detection, enable the opt-in hook described in Optional: Enable Hook.

Quick Reference

OpenClaw Setup

OpenClaw uses workspace-based prompt injection with automatic skill loading.

Installation

Via ClawdHub (recommended):

Manual:

Remade for openclaw from original repo : https://github.com/pskoett/pskoett-ai-skills - https://github.com/pskoett/pskoett-ai-skills/tree/main/skills/self-improvement

Workspace Structure

OpenClaw injects these files into every session:

Create Learning Files

Then create the log files (or copy from assets/):

• LEARNINGS.md — corrections, knowledge gaps, best practices
• ERRORS.md — command failures, exceptions
• FEATURE_REQUESTS.md — user-requested capabilities

Promotion Targets

When learnings prove broadly applicable, promote them to workspace files:

Inter-Session Communication

OpenClaw provides tools to share learnings across sessions:

• sessions_list — View active/recent sessions
• sessions_history — Read another session's transcript
• sessions_send — Send a learning to another session
• sessions_spawn — Spawn a sub-agent for background work

Use these only in trusted environments and only when the user explicitly wants cross-session sharing. Prefer sending a short sanitized summary and relevant file paths, not raw transcripts, secrets, or full command output.

Optional: Enable Hook

For automatic reminders at session start and error detection at session end:

Fires on agent:bootstrap (injects the reminder, plus a pending-triage note when auto-detected errors await review) and on command:new/command:reset (sweeps the ended session's transcript for error patterns into <workspace>/.learnings/ERRORS.md; opt-in — runs only when .learnings/ exists). OpenClaw has no per-tool-call hook event, so error detection happens at session end. See references/openclaw-integration.md for details and sweep limitations.

Logging Format

Learning Entry

Append to .learnings/LEARNINGS.md:

Error Entry

Append to .learnings/ERRORS.md:

Actual error message or output

Feature Request Entry

Append to .learnings/FEATURE_REQUESTS.md:

ID Generation

Format: TYPE-YYYYMMDD-XXX

• TYPE: LRN (learning), ERR (error), FEAT (feature)
• YYYYMMDD: Current date
• XXX: Sequential number or random 3 chars (e.g., 001, A7B)

Examples: LRN-20250115-001, ERR-20250115-A3F, FEAT-20250115-002

Resolving Entries

When an issue is fixed, update the entry:

1. Change **Status**: pending → **Status**: resolved
2. Add resolution block after Metadata:

Other status values:

• in_progress - Actively being worked on
• wont_fix - Decided not to address (add reason in Resolution notes)
• promoted - Elevated to a workspace file (SOUL.md, TOOLS.md, AGENTS.md)

Promoting to Workspace Memory

When a learning is broadly applicable (not a one-off fix), promote it to a workspace file so every session inherits it.

When to Promote

• Learning applies across multiple files/features
• Knowledge any contributor (human or AI) should know
• Prevents recurring mistakes
• Documents project-specific conventions

Promotion Targets

When the learning is specific to a project repo you work in (not the workspace), promote to that project's own agent file (e.g. its AGENTS.md) instead.

How to Promote

1. Distill the learning into a concise rule or fact
2. Add to appropriate section in target file (create file if needed)
3. Update original entry:
   • Change **Status**: pending → **Status**: promoted
   • Add **Promoted**: SOUL.md, TOOLS.md, or AGENTS.md

Promotion Examples

Learning (verbose):

Project uses pnpm workspaces. Attempted npm install but failed. Lock file is pnpm-lock.yaml. Must use pnpm install.

In TOOLS.md (concise):

Learning (verbose):

When modifying API endpoints, must regenerate TypeScript client. Forgetting this causes type mismatches at runtime.

In AGENTS.md (actionable):

Pattern-Key Taxonomy

Pattern-Key is the stable dedup and recurrence key for entries in all three log files: keyword grep misses semantically identical but differently-worded entries, a shared key does not — and reliable keys are what make Recurrence-Count and the promotion rule work.

Format: area.symptom — exactly two levels, lowercase, hyphenated (e.g. deps.module-not-found). Keep symptoms generic enough to recur: no file names, versions, or hostnames in keys.

Rules:

1. Reuse before minting: grep -rh "Pattern-Key:" .learnings/ | sort -u — a near-match beats a new key.
2. One key per manual entry; auto-swept OpenClaw entries may carry several — reduce to one when triaging.
3. Mint new areas sparingly — only when several entries would share one.
4. Generic sweep keys (runtime.error, runtime.failure) mean "unclassified" — replace with a specific key during triage.

Recurring Pattern Detection

If logging something similar to an existing entry:

1. Search by key first: grep -n "Pattern-Key: area.symptom" .learnings/*.md — this is the default dedup check and catches rewordings that keyword search misses
2. Fallback keyword search: grep -ri "keyword" .learnings/ for entries logged without a key
3. Fold, don't duplicate: on a hit, update the existing entry — bump Recurrence-Count, set Last-Seen, add **See Also** — instead of creating a new one
4. Bump priority if issue keeps recurring
5. Consider systemic fix: Recurring issues often indicate:
   • Missing knowledge (→ promote to TOOLS.md or SOUL.md)
   • Missing automation (→ add to AGENTS.md)
   • Architectural problem (→ create tech debt ticket)

Simplify & Harden Feed

Use this workflow to ingest recurring patterns from the simplify-and-harden skill and turn them into durable prompt guidance.

Ingestion Workflow

1. Read simplify_and_harden.learning_loop.candidates from the task summary.
2. For each candidate, use pattern_key as the stable dedupe key.
3. Search .learnings/LEARNINGS.md for an existing entry with that key:
   • grep -n "Pattern-Key: <pattern_key>" .learnings/LEARNINGS.md
4. If found:
   • Increment Recurrence-Count
   • Update Last-Seen
   • Add See Also links to related entries/tasks
5. If not found:
   • Create a new LRN-... entry
   • Set Source: simplify-and-harden
   • Set Pattern-Key, Recurrence-Count: 1, and First-Seen/Last-Seen

Promotion Rule (System Prompt Feedback)

Promote recurring patterns into agent context/system prompt files when all are true:

• Recurrence-Count >= 3
• Seen across at least 2 distinct tasks
• Occurred within a 30-day window

Promotion targets: SOUL.md, TOOLS.md, or AGENTS.md (workspace), or the project's own agent file when the pattern is project-specific.

Write promoted rules as short prevention rules (what to do before/while coding), not long incident write-ups.

Periodic Review

Review .learnings/ at natural breakpoints:

When to Review

• Before starting a new major task
• After completing a feature
• When working in an area with past learnings
• Weekly during active development

Quick Status Check

Review Actions

• Resolve fixed items
• Promote applicable learnings
• Link related entries
• Escalate recurring issues

Detection Triggers

Automatically log when you notice:

Corrections (→ learning with correction category):

• "No, that's not right..."
• "Actually, it should be..."
• "You're wrong about..."
• "That's outdated..."

Feature Requests (→ feature request):

• "Can you also..."
• "I wish you could..."
• "Is there a way to..."
• "Why can't you..."

Knowledge Gaps (→ learning with knowledge_gap category):

• User provides information you didn't know
• Documentation you referenced is outdated
• API behavior differs from your understanding

Errors (→ error entry):

• Command returns non-zero exit code
• Exception or stack trace
• Unexpected output or behavior
• Timeout or connection failure

Priority Guidelines

Area Tags

Use to filter learnings by codebase region:

Best Practices

1. Log immediately - context is freshest right after the issue
2. Be specific - future agents need to understand quickly
3. Include reproduction steps - especially for errors
4. Link related files - makes fixes easier
5. Suggest concrete fixes - not just "investigate"
6. Use consistent categories - enables filtering
7. Promote aggressively - if in doubt, add to TOOLS.md or SOUL.md
8. Review regularly - stale learnings lose value

Gitignore Options

Keep learnings local (per-developer):

This repo uses that default to avoid committing sensitive or noisy local logs by accident.

Track learnings in repo (team-wide): Don't add to .gitignore - learnings become shared knowledge.

Hybrid (track templates, ignore entries):

Upgrading & Uninstalling

Read CHANGELOG.md before upgrading — it carries per-version notes, and hook changes require re-copying the hook and restarting the gateway. To disable or remove the skill, follow references/uninstall.md: .learnings/ is user data (review before deleting), and content promoted to SOUL.md/TOOLS.md/AGENTS.md stays until removed manually.

Automatic Skill Extraction

When a learning is valuable enough to become a reusable skill, extract it using the provided helper.

Skill Extraction Criteria

A learning qualifies for skill extraction when ANY of these apply:

Extraction Workflow

1. Identify candidate: Learning meets extraction criteria
2. Run helper (or create manually):

   ~/.openclaw/skills/self-improving-agent/scripts/extract-skill.sh skill-name --dry-run
   ~/.openclaw/skills/self-improving-agent/scripts/extract-skill.sh skill-name
3. Customize SKILL.md: Fill in template with learning content
4. Update learning: Set status to promoted_to_skill, add Skill-Path
5. Verify: Read skill in fresh session to ensure it's self-contained

Manual Extraction

If you prefer manual creation:

1. Create skills/<skill-name>/SKILL.md
2. Use template from assets/SKILL-TEMPLATE.md
3. Follow Agent Skills spec:
   • YAML frontmatter with name and description
   • Name must match folder name
   • No README.md inside skill folder

Extraction Detection Triggers

Watch for these signals that a learning should become a skill:

In conversation:

• "Save this as a skill"
• "I keep running into this"
• "This would be useful for other projects"
• "Remember this pattern"

In learning entries:

• Multiple See Also links (recurring issue)
• High priority + resolved status
• Category: best_practice with broad applicability
• User feedback praising the solution

Skill Quality Gates

Before extraction, verify:

• Solution is tested and working
• Description is clear without original context
• Code examples are self-contained
• No project-specific hardcoded values
• Follows skill naming conventions (lowercase, hyphens)