Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Tyuts/xiaobai-skills --skill self-improving-agentgit clone --depth 1 https://github.com/Tyuts/xiaobai-skillsWrote 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/tyuts/xiaobai-skills/self-improving-agent)<a href="https://agentmods.dev/skills/tyuts/xiaobai-skills/self-improving-agent"><img src="https://agentmods.dev/badge/skills/tyuts/xiaobai-skills/self-improving-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tyuts/xiaobai-skills/self-improving-agent"><img src="https://agentmods.dev/badge/skills/tyuts/xiaobai-skills/self-improving-agent.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00068 | $0.00654 |
| Opus 5 | $0.00034 | $0.00327 |
| Sonnet 5 | $0.00014 | $0.00131 |
| Haiku 4.5 | $0.00007 | $0.00065 |
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 9d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Improving Agent
This is a repo-local skill for Xiaobai Skills. It helps the project learn from real work without turning every task into expensive reflection.
Use it only when one of these happens:
- A user reports an install or curation failure.
- A promotion experiment teaches something reusable.
- The recommended starter stack changes.
- The agent repeatedly searches for the same repo knowledge.
- A workflow repeats enough to deserve a repo-local skill.
- A release reveals missing docs, checks, or restore steps.
Do not use it after every small edit.
Step 1: Diagnose The Learning
Ask:
- Did the repo structure or command flow change?
- Did the user correct a behavior that should persist?
- Did an install command fail in a way future agents should know?
- Did a starter-stack decision prove good or bad?
- Did a repeated workflow emerge?
If the answer is no, make no changes.
Step 2: Choose The Right Artifact
- Update
AGENTS.mdfor short repo rules, commands, gotchas, or decision history. - Update
README.mdfor user-facing install or positioning changes. - Update
ROADMAP.mdfor future work. - Update
docs/promotion-kit.mdfor launch and marketing learnings. - Create
skills/<name>/SKILL.mdonly for repeatable multi-step workflows.
Prefer one concise update over several files.
Step 3: Update AGENTS.md Safely
When editing AGENTS.md:
- Keep entries short and repo-specific.
- Add dated decision notes only when they explain future behavior.
- Remove or revise stale notes instead of appending contradictions.
- Keep the file readable for every future session.
- Never add secrets, payment details, tokens, or private user data.
Step 4: Create Repo-Local Skills Sparingly
Create a new skill only when the workflow is:
- repeated
- multi-step
- specific enough to benefit from written procedure
- useful for future maintainers
Use:
skills/<skill-name>/SKILL.md
The skill must include:
- clear
name - clear
description - trigger conditions
- steps
- output contract
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.
- 9d ago First seen · 96 lines · 68 tokens per session scan A d0f49adfaf54
self-improving-agent is a skill published in the GitHub repository Tyuts/xiaobai-skills (59 stars, last pushed 3mo ago), licensed MIT. It adds 68 tokens to every session and 654 once invoked, about $0.0003 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.
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