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 zhaono1/agent-playbook --skill self-improving-agentgit clone --depth 1 https://github.com/zhaono1/agent-playbookWrote 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/zhaono1/agent-playbook/self-improving-agent)<a href="https://agentmods.dev/skills/zhaono1/agent-playbook/self-improving-agent"><img src="https://agentmods.dev/badge/skills/zhaono1/agent-playbook/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/zhaono1/agent-playbook/self-improving-agent"><img src="https://agentmods.dev/badge/skills/zhaono1/agent-playbook/self-improving-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 10 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00048 | $0.01728 |
| Opus 5.5 | $0.00019 | $0.00691 |
| Sonnet 5.5 | $0.00010 | $0.00346 |
| Haiku 4.5 | $0.00005 | $0.00173 |
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 1mo 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Improving Agent
Turn evidence from completed work into a small, auditable behavior change. The default result is a candidate or no change—not an automatic rewrite of skills.
Use This Skill When
- A tool or workflow failed in a way that may recur.
- The user corrected an assumption, requirement, or operating rule.
- The same workaround appeared more than once.
- A focused test proved a better reusable method.
- The user asks to review or consolidate learning candidates.
Do not use it for routine session summaries, raw transcript storage, speculative ideas without evidence, or project facts that belong in project documentation.
Required Outcome
Every run ends in exactly one state:
candidate: reusable but not yet validated.validated: representative evidence supports the lesson, but no owner change is claimed yet.applied: the validated lesson was installed in one named durable owner with a change reference.rejected: disproved, unsafe, too specific, or obsolete.supersededorrolled_back: an applied/validated lesson was replaced or reverted.no-delta: no reusable behavior change was found.open-question: evidence is insufficient and the missing proof is named.
An artifact is not proof of improvement. An applied lesson must change future behavior and have a representative check that demonstrates the change.
Start Packet
Before editing durable guidance, state:
- Future behavior: what the agent should do differently next time.
- Representative task: one concrete scenario that should now succeed.
- Evidence: current source, failure output, user correction, or focused test.
- Owner: the one skill, instruction file, script, or runtime component that owns it.
- Write boundary: files allowed to change and information that must remain local.
- Proof: the command, eval, or review that confirms the new behavior.
If any item is unknown, capture a candidate and stop before validation or application.
Lifecycle
1. Capture the Signal
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 215 lines · 48 tokens per session scan A dd012c10ecf4
self-improving-agent is a skill published in the GitHub repository zhaono1/agent-playbook (85 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 1,728 once invoked, about $0.0002 per session on Opus 5.5. 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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