self-improving-agent

name	self-improving-agent
description	Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
version	4.0.2
metadata	null

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 "# Errors\n\nCommand failures and integration errors.\n\n---\n" > .learnings/ERRORS.md
[ -f .learnings/FEATURE_REQUESTS.md ] || printf "# Feature Requests\n\nCapabilities requested by the user.\n\n---\n" > .learnings/FEATURE_REQUESTS.md

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

Situation	Action
Command/operation fails	Log to .learnings/ERRORS.md
User corrects you	Log to .learnings/LEARNINGS.md with category correction
User wants missing feature	Log to .learnings/FEATURE_REQUESTS.md
API/external tool fails	Log to .learnings/ERRORS.md with integration details
Knowledge was outdated	Log to .learnings/LEARNINGS.md with category knowledge_gap
Found better approach	Log to .learnings/LEARNINGS.md with category best_practice
Simplify/Harden recurring patterns	Log/update .learnings/LEARNINGS.md with Source: simplify-and-harden and a stable Pattern-Key
Similar to existing entry	Grep by Pattern-Key first, link with **See Also**, bump Recurrence-Count
Workflow improvements	Promote to AGENTS.md (workspace)
Tool gotchas	Promote to TOOLS.md (workspace)
Behavioral patterns	Promote to SOUL.md (workspace)

OpenClaw Setup

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

Installation

Via OpenClaw's built-in installer (recommended) — installs into the active OpenClaw workspace:

openclaw skills install @pskoett/self-improving-agent

Via the ClawHub CLI (npm i -g clawhub) — installs into ./skills under the current working directory, not the workspace:

clawhub install @pskoett/self-improving-agent

Manual (the skill lives in the repo's self-improving-agent/ subfolder; copy that folder, not the repo root):

git clone https://github.com/pskoett/self-improving-agent.git /tmp/self-improving-agent-repo
cp -r /tmp/self-improving-agent-repo/self-improving-agent ~/.openclaw/skills/self-improving-agent

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:

~/.openclaw/workspace/
├── AGENTS.md          # Multi-agent workflows, delegation patterns
├── SOUL.md            # Behavioral guidelines, personality, principles
├── TOOLS.md           # Tool capabilities, integration gotchas
├── MEMORY.md          # Long-term memory (main session only)
├── memory/            # Daily memory files
│   └── YYYY-MM-DD.md
└── .learnings/        # This skill's log files
    ├── LEARNINGS.md
    ├── ERRORS.md
    └── FEATURE_REQUESTS.md

Create Learning Files

mkdir -p ~/.openclaw/workspace/.learnings

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:

Learning Type	Promote To	Example
Behavioral patterns	SOUL.md	"Be concise, avoid disclaimers"
Workflow improvements	AGENTS.md	"Spawn sub-agents for long tasks"
Tool gotchas	TOOLS.md	"Git push needs auth configured first"

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:

cp -r ~/.openclaw/skills/self-improving-agent/hooks/openclaw ~/.openclaw/hooks/self-improvement
openclaw hooks enable self-improvement

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:

## [LRN-YYYYMMDD-XXX] category

**Logged**: ISO-8601 timestamp
**Priority**: low | medium | high | critical
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config

### Summary
One-line description of what was learned

### Details
Full context: what happened, what was wrong, what's correct

### Suggested Action
Specific fix or improvement to make

### Metadata
- Source: conversation | error | user_feedback
- Related Files: path/to/file.ext
- Tags: tag1, tag2
- See Also: LRN-20250110-001 (if related to existing entry)
- Pattern-Key: area.symptom (recommended; e.g. deps.module-not-found, simplify.dead_code — see Pattern-Key Taxonomy)
- Recurrence-Count: 1 (optional)
- First-Seen: 2025-01-15 (optional)
- Last-Seen: 2025-01-15 (optional)

---

Error Entry

Append to .learnings/ERRORS.md:

## [ERR-YYYYMMDD-XXX] skill_or_command_name

**Logged**: ISO-8601 timestamp
**Priority**: high
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config

### Summary
Brief description of what failed

### Error

Actual error message or output

### Context
- Command/operation attempted
- Input or parameters used
- Environment details if relevant
- Summary or redacted excerpt of relevant output (avoid full transcripts and secret-bearing data by default)

### Suggested Fix
If identifiable, what might resolve this

### Metadata
- Reproducible: yes | no | unknown
- Related Files: path/to/file.ext
- See Also: ERR-20250110-001 (if recurring)
- Pattern-Key: area.symptom (recommended; e.g. net.connection-refused — see Pattern-Key Taxonomy)
- Recurrence-Count: 1 (optional)
- First-Seen: 2025-01-15 (optional)
- Last-Seen: 2025-01-15 (optional)

---

Feature Request Entry

Append to .learnings/FEATURE_REQUESTS.md:

## [FEAT-YYYYMMDD-XXX] capability_name

**Logged**: ISO-8601 timestamp
**Priority**: medium
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config

### Requested Capability
What the user wanted to do

### User Context
Why they needed it, what problem they're solving

### Complexity Estimate
simple | medium | complex

### Suggested Implementation
How this could be built, what it might extend

### Metadata
- Frequency: first_time | recurring
- Related Features: existing_feature_name
- Pattern-Key: area.symptom (optional — features usually dedupe by capability name; use a key only for recurring themes, e.g. api.missing-endpoint)

---

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:

### Resolution
- **Resolved**: 2025-01-16T09:00:00Z
- **Commit/PR**: abc123 or #42
- **Notes**: Brief description of what was done

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

Target	What Belongs There
SOUL.md	Behavioral guidelines, communication style, principles
TOOLS.md	Tool capabilities, usage patterns, integration gotchas
AGENTS.md	Workflows, delegation patterns, automation rules

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):

## Build & Dependencies
- Package manager: pnpm (not npm) - use `pnpm install`

Learning (verbose):

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

In AGENTS.md (actionable):

## After API Changes
1. Regenerate client: `pnpm run generate:api`
2. Check for type errors: `pnpm tsc --noEmit`

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.

Area	Scope	Example Keys
api	External API/service behavior	api.rate-limit, api.schema-mismatch, api.missing-endpoint
auth	Credentials, tokens, scopes	auth.token-expired, auth.missing-scope
build	Compilation, bundling, CI	build.type-error, build.missing-artifact
config	Config files, env vars, settings	config.missing-env, config.invalid-json
deps	Package managers, dependencies	deps.module-not-found, deps.npm-error, deps.version-conflict
fs	Filesystem	fs.no-such-file, fs.permission-denied
net	Network connectivity	net.connection-refused, net.timeout
runtime	Language/runtime errors not covered above	runtime.type-error, runtime.python-exception
shell	Shell/CLI mechanics	shell.command-not-found, shell.nonzero-exit
vcs	Git and other version control	vcs.fatal-error, vcs.merge-conflict