Borrowing it
Nothing to install: this file belongs to mick-gsk/drift. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mick-gsk/drift/main/.github/skills/self-improving-agent/SKILL.mdgit clone --depth 1 https://github.com/mick-gsk/driftWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mick-gsk/drift/self-improving-agent)<a href="https://agentmods.dev/skills/mick-gsk/drift/self-improving-agent"><img src="https://agentmods.dev/badge/skills/mick-gsk/drift/self-improving-agent.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00085 | $0.02337 |
| Opus 5 | $0.00043 | $0.01169 |
| Sonnet 5 | $0.00017 | $0.00467 |
| Haiku 4.5 | $0.00009 | $0.00234 |
Grade A, and why
self-improving-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Improvement Skill
Log learnings and errors to markdown files for continuous improvement. Coding agents can later process these into fixes, and important learnings get promoted to project memory.
Source: https://clawhub.ai/pskoett/self-improving-agent (v3.0.16, MIT-0)
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.
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 | Link with See Also, consider priority bump |
| Broadly applicable learning | Promote to CLAUDE.md, AGENTS.md, and/or .github/copilot-instructions.md |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 296 lines · 85 tokens per session scan A 379c19017c27
self-improving-agent is a skill published in the GitHub repository mick-gsk/drift (15 stars, last pushed 1mo ago), licensed MIT. It adds 85 tokens to every session and 2,337 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
init-workspace-documentation
Skill "init-workspace-documentation" from griddynamics/rosetta, covering agent memory.md, agent memory, preventive rules, what worked and what failed.
gohm
/gohm - Go Harvest Memory (Knowledge Harvester).
memory
Use this agent to manage permanent memory storage, retrieval weight calculation, and knowledge preservation following NASAB Pillar 5.
context
Context Manager - Token Budget Controller.
recall
/recall — Layered Knowledge Recall.
evolve
/evolve - Framework Evolution from Dev-Memory.