Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aaronjmars/aeon-agentnpx agentmods add skills/aaronjmars/aeon-agent/self-improveWrote 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/aaronjmars/aeon-agent/self-improve)<a href="https://agentmods.dev/skills/aaronjmars/aeon-agent/self-improve"><img src="https://agentmods.dev/badge/skills/aaronjmars/aeon-agent/self-improve.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 | $0.00044 | $0.02136 |
| Opus 5 | $0.00022 | $0.01068 |
| Sonnet 5 | $0.00009 | $0.00427 |
| Haiku 4.5 | $0.00004 | $0.00214 |
Grade A, and why
self-improve 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 2d 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.
This is a copy
100% identical to self-improve — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
${var} — Mode selector, optionally with a focus area, as
modeormode: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 likenotifications) → 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 ${var} into a mode and an optional focus area:
- Split on the first
:— the part before is the mode, the part after is the focus. - If the mode is
audit→ run the Mode: audit branch below (focus = optional area to concentrate the review on). - If the mode is
improveor empty → run the Mode: improve branch below (focus = optional area to fix). - If the token is neither keyword but non-empty (e.g.
notifications) → treat it as improve mode with the whole${var}as the focus area (backward compatibility).
Then:
- Read
memory/MEMORY.mdfor 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
-
Check for open improvement PRs — don't pile up unreviewed work:
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.
-
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.jsonfor skills with low success rates - Read
output/articles/repo-actions-*.mdfrom last 7 days for self-improvement ideas - Pick the highest-impact, smallest-effort fix. One change per run.
- Read
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.
- 2d ago First seen · 194 lines · 44 tokens per session scan A 0ba317e2fba4
self-improve is a skill published in the GitHub repository aaronjmars/aeon-agent (11 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 2,136 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to self-improve, differing in 0 lines, and is treated as a copy.
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