Agent skill

Self Improve

by aeonfun in aeonfun/aeon

Improve the agent itself, or audit its recent performance - better skills, prompts, workflows, and config, plus a quality/reliability/memory-hygiene review of what it did and what failed

MITAuto-check passed

Install Self Improve

skills CLI
$ npx skills add aeonfun/aeon --skill self-improve -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aeonfun/aeon self-improve --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aeonfun/aeon.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/self-improve .claude/skills/self-improve && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
self-improve
GitHub stars
767
Token cost
~2.1k tokens
SKILL.md length
913 words
Files
1
Skills in repo
82
Repo updated
First seen
Licence
MIT

At a glance

Improve the agent itself, or audit its recent performance - better skills, prompts, workflows, and config, plus a quality/reliability/memory-hygiene review of what it did and what failed

  • Works in 4 steps: Check for open improvement PRs — don't… → Identify what to improve. If the focus… → Understand the area you're fixing. Read… → …
  • SKILL.md covers Setup (both modes), Mode: improve (default), Mode: audit and Log, plus 1 more section
  • Calls gh, git and bash

What it does

Self Improve is an agent skill from aeonfun/aeon. Improve the agent itself, or audit its recent performance - better skills, prompts, workflows, and config, plus a quality/reliability/memory-hygiene review of what it did and what failed

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: The most autonomous AI agent framework: runs unattended on GitHub Actions, self-healing skills, drives Claude Code, Grok, Codex & more. No approval loops. Configure once, forget… The licence is MIT.

Example prompts

  • “/self-improve”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Check for open improvement PRs — don't pile up unreviewed work
  2. Identify what to improve. If the focus area is empty, scan for issues
  3. Understand the area you're fixing. Read the relevant files
  4. Implement the fix. Make minimal, targeted changes

What it can do on your machine

Read from SKILL.md and the folder at commit c0cb7c4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gh
    • git
    • bash

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gh and git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Self Improve loads about 2.1k tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 913 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aeonfun/aeon at commit c0cb7c4, republished under its MIT licence (© aeonfun). 913 words, ~2,083 tokens.

Download SKILL.mdSave it as .claude/skills/self-improve/SKILL.md (or your agent's skills folder).
name
self-improve
description
Improve the agent itself, or audit its recent performance - better skills, prompts, workflows, and config, plus a quality/reliability/memory-hygiene review of what it did and what failed
metadata.category
evolution
metadata.tags
meta

${var} — Mode selector, optionally with a focus area, as mode or mode:focus.

  • empty or improve → improve mode: find and fix the highest-impact issue from recent logs, then propose + apply the fix via PR (default).
  • improve:<area> (or a bare area like notifications) → improve mode focused on that specific area (e.g. heartbeat, notifications, memory).
  • audit → audit mode: review what the agent did, what failed, and what to improve; save a full review and apply safe, obvious fixes directly.
  • audit:<area> → audit mode focused on that specific area (e.g. reliability, memory).

Setup (both modes)

Parse ${var} into a mode and an optional focus area:

  • Split on the first : — the part before is the mode, the part after is the focus.
  • If the mode is audit → run the Mode: audit branch below (focus = optional area to concentrate the review on).
  • If the mode is improve or empty → run the Mode: improve branch below (focus = optional area to fix).
  • If the token is neither keyword but non-empty (e.g. notifications) → treat it as improve mode with the whole ${var} as the focus area (backward compatibility).

Then:

  • Read memory/MEMORY.md for high-level context and goals.
  • Read recent memory/logs/ (improve mode: last 2 days; audit mode: last 7 days) for errors, failures, and quality issues.

If a focus area is set, concentrate the run on that area.


Mode: improve (default)

Improve the agent itself based on recent performance. ONE change per run.

Steps
  1. Check for open improvement PRs — don't pile up unreviewed work:

    bash
    OPEN_PRS=$(gh pr list --state open --json title,number --jq '[.[] | select(.title | test("^(fix|feat|chore)\\("; "i"))] | length')

    If there are already 3+ open improvement PRs, log "self-improve: 3+ open PRs, waiting for review" and exit. Don't create more debt.

  2. Identify what to improve. If the focus area is empty, scan for issues:

    • Read memory/logs/ from last 2 days — look for:
      • Skills that failed or produced low-quality output
      • Errors, timeouts, "zero output", rate limiting
      • Notifications that didn't send or were truncated
      • Memory consolidation problems
    • Read memory/cron-state.json for skills with low success rates
    • Read output/articles/repo-actions-*.md from last 7 days for self-improvement ideas
    • Pick the highest-impact, smallest-effort fix. One change per run.
  3. Understand the area you're fixing. Read the relevant files:

    • Skills: skills/{name}/SKILL.md
    • Config: aeon.yml
    • Workflows: .github/workflows/*.yml
    • Agent instructions: CLAUDE.md
    • Dashboard: apps/dashboard/ (if UI-related)

    Understand the current behavior before changing anything.

  4. Implement the fix. Make minimal, targeted changes:

    • If a skill prompt is unclear → rewrite the ambiguous section
    • If a skill is hitting rate limits → add backoff logic or reduce frequency
    • If output quality is low → tighten the prompt, add examples, clarify format
    • If a notification is broken → fix the formatting or truncation
    • If a config is wrong → fix aeon.yml

    Do NOT:

    • Rewrite entire skills from scratch
    • Add new features (that's create-skill's job)
    • Change the core architecture
    • Modify secrets or environment variables

4b. Dry-run gate. Before opening the PR, execute the improved skill once with synthetic secrets, so a regression never reaches production having run only with real credentials. Let $skill be the skill you edited:

bash
DRYRUN_VERDICT="output/.dry-run/$skill.json" bash scripts/dry-run.sh run "$skill" || true

Read output/.dry-run/$skill.json: passed: true (or skipped: true, when the SKILL_DRYRUN repo variable is 0) continues. passed: false means revert the edit and stop (log self-improve: dry-run gate failed for $skill with the verdict reasons[]; do not open the PR). Put the verdict under a ## Dry-run section in the PR body. The gate is structural (exit, output, declared mode, declared requires:); no real credential enters the run.

  1. Create a branch and PR:

    bash
    git checkout -b fix/self-improve-${today}
    git add -A
    git commit -m "fix: [description of what was improved]
    
    Problem: [what was failing/degraded]
    Fix: [what was changed]
    Evidence: [log entries, error messages, success rates]"

    Open a PR:

    bash
    gh pr create --title "fix: [short description]" \
      --body "## Problem
    [What was failing or degraded — cite specific log entries or error messages]
    
    ## Fix
    [What was changed and why]
    
    ## Evidence
    - [Relevant log entries]
    - [Success rate before: X%]
    - [Error pattern: ...]"
  2. Notify. Send via ./notify:

    self-improve: [what was fixed] — PR: [url]
  3. Log (see the shared ## Log section below).

Show full SKILL.md (360 more words)Show less
Guidelines
  • ONE fix per run. Don't bundle unrelated changes.
  • Smallest viable fix. A one-line prompt tweak > a full rewrite.
  • If you can't find anything to improve, that's fine. Log "self-improve: everything looks healthy" and exit.
  • Never modify workflow files (.github/workflows/) — only skill files, CLAUDE.md, and aeon.yml.
  • Don't create circular improvements (e.g. don't improve self-improve).

Mode: audit

Audit what the agent did, what failed, and what to improve. Produce a full review, apply safe fixes, and surface recommendations.

Steps

Read memory/MEMORY.md for context and goals. Read ALL memory/logs/ entries from the last 7 days.

  1. Audit quality of outputs:
    • Read recent articles in output/articles/ — are they substantive or formulaic?
    • Check recent notifications in logs — were they useful or noisy?
    • Review any PR comments posted — were they actionable?
  2. Audit reliability:
    • How many skills ran vs expected?
    • Any repeated errors or patterns of failure?
    • Are monitors catching real issues or always returning OK?
  3. Audit memory hygiene:
    • Is MEMORY.md current and under 50 lines?
    • Are logs structured consistently?
    • Any stale data that should be cleaned?
  4. Generate improvement recommendations:
    • Skills to add, modify, or disable
    • Schedule adjustments
    • Config changes (feeds, repos, addresses to add/remove)
    • Quality improvements (better prompts, new data sources)
  5. Save the full review to output/articles/self-review-${today}.md.
  6. Apply any safe, obvious improvements directly:
    • Prune stale MEMORY.md entries
    • Update feeds.yml if feeds are dead
  7. Send a summary via ./notify:
    *Self Review — ${today}*
    Quality: assessment
    Reliability: X/Y skills ran
    Actions taken: what was fixed
    Recommendations: top 2-3 suggestions
  8. Log (see the shared ## Log section below).

Log

After completing the run, append a log entry to memory/logs/${today}.md under a single ### self-improve heading, with a discriminator line naming the mode that ran:

### self-improve
- **Mode:** improve  (or: audit)

Then, for improve mode:

- **Target:** [what was improved]
- **Problem:** [what was failing]
- **Fix:** [what was changed]
- **PR:** [url]

For audit mode:

- **Review:** output/articles/self-review-${today}.md
- **Quality:** [assessment]
- **Reliability:** [X/Y skills ran]
- **Actions taken:** [what was fixed directly]
- **Recommendations:** [top 2-3]

Network note

Write mode. Both branches touch the repo (improve opens a PR via git/gh; audit writes output/articles/self-review-${today}.md and may prune MEMORY.md/feeds.yml). For the GitHub API, use the gh CLI (gh pr list, gh pr create) — it handles auth internally, so no $SECRET ever touches the command line (a bare secret on the line is what the Bash permission layer refuses; there is no network sandbox). No pre-fetch or post-process side-channel is needed.

© aeonfun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/self-improve of aeonfun/aeon.

Open the folder on GitHubat commit c0cb7c4

Compare with similar skills

Self Improve next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Self Improve compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Self Improve this skillaeonfun/aeon767—~2.1kAutomated safety check: PassMIT
Better Interfacetrycompai/crm11k—~2kAutomated safety check: PassMIT
Skill Improversickn33/agentic-awesome-skills47k2 repos~1.5kAutomated safety check: PassMIT
Better Proposals AutomationComposioHQ/awesome-claude-skills77k3 repos~764Automated safety check: PassNone
Better Stack AutomationComposioHQ/awesome-claude-skills77k3 repos~749Automated safety check: PassNone
Improve Oaselastic/kibana21k—~4.8kAutomated safety check: PassCustom licence

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Questions about Self Improve

What does Self Improve do?

Improve the agent itself, or audit its recent performance - better skills, prompts, workflows, and config, plus a quality/reliability/memory-hygiene review of what it did and what failed. Self Improve is an agent skill from aeonfun/aeon.

How do I install Self Improve in Claude Code?

Run `npx skills add aeonfun/aeon --skill self-improve -a claude-code`. Or copy the skill folder (skills/self-improve in aeonfun/aeon) into .claude/skills/self-improve in your project. Claude Code loads it when a task matches its description.

How do I install Self Improve in Codex?

Run `npx skills add aeonfun/aeon --skill self-improve -a codex`. Or copy the skill folder (skills/self-improve in aeonfun/aeon) into .agents/skills/self-improve in your project. Codex loads it when a task matches its description.

Can I use Self Improve in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aeonfun/aeon --skill self-improve -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/self-improve, .gemini/skills/self-improve, .github/skills/self-improve and .opencode/skills/self-improve in your project.

What does Self Improve need to run?

Going by SKILL.md and its folder, Self Improve needs the command-line tools its instructions call (gh, git and bash).

Does Self Improve access the network?

SKILL.md contains no URLs. Its commands use gh and git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Self Improve safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Self Improve use?

Self Improve is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Self Improve use?

About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Self Improve?

Skills that share tags, products or a category with Self Improve: Better Interface (trycompai/crm, 11k stars), Skill Improver (sickn33/agentic-awesome-skills, 47k stars), Better Proposals Automation (ComposioHQ/awesome-claude-skills, 77k stars) and Better Stack Automation (ComposioHQ/awesome-claude-skills, 77k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Self Improve?

aeonfun (a GitHub organization) maintains it in aeonfun/aeon, which has 767 GitHub stars. The repository holds 82 skills in this directory. The repository was last updated on October 8, 2026.

Source: aeonfun/aeon on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.