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 agentmods add skills/jnmetacode/skillet/self-evolvenpx skills add jnMetaCode/skillet --skill self-evolvegit clone --depth 1 https://github.com/jnMetaCode/skilletWrote 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/jnmetacode/skillet/self-evolve)<a href="https://agentmods.dev/skills/jnmetacode/skillet/self-evolve"><img src="https://agentmods.dev/badge/skills/jnmetacode/skillet/self-evolve.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 | $0.00052 | $0.00855 |
| Opus 5 | $0.00026 | $0.00428 |
| Sonnet 5 | $0.00010 | $0.00171 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
self-evolve 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 4d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
self-evolve
An agent that doesn't learn repeats its mistakes at full price. This skill is the improvement loop: observe the run → distill the lesson → store it → reinforce what worked → recall it next time. It uses local tools (engram for memory, tracelet for observation), so the learning stays on the user's machine.
The loop (run it at the end of each non-trivial task)
-
Observe what actually happened. Don't trust your memory of the run — check it. If tracing is on (
npx @jnmetacode/tracelet), look at the trace: which tool calls failed or were retried, where the time and tokens went, which approach was abandoned. Without a trace, review your own steps: what did you try first that didn't work? -
Distill at most 1–3 lessons. A lesson is durable knowledge that would change how you act next time, not a diary entry:
- a root cause ("the staging DB rejects connections without SSL")
- a working recipe ("build fails unless
npm ciruns before codegen") - a user preference revealed by a correction ("they want diffs, not files") Skip anything the repo/docs already record.
-
Store each lesson (one fact per memory, dated, with the why):
engram_remember: "2026-06-12: deploys to staging need SSL_MODE=require — the pooler silently drops non-SSL connections (cost us 40 min)."(CLI:
npx @jnmetacode/engram remember …via the HTTP API, or theengram_rememberMCP tool.) -
Reinforce retrievals that proved right. If you recalled a memory during the task and it turned out to be the correct answer, say so — recall gets measurably better with use:
engram_reinforce: { query: "staging deploy fails", source: "deploys" }Future similar queries will rank that source higher. Only reinforce what was verified (the fix worked, the user confirmed) — reinforcing guesses trains the memory to be confidently wrong.
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.
- 4d ago First seen · 68 lines · 52 tokens per session scan A b6c8f35749ce
self-evolve is a skill published in the GitHub repository jnMetaCode/skillet (1 stars, last pushed 2mo ago), licensed MIT. It adds 52 tokens to every session and 855 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 2026-08-31.
Other skills, from other repositories
autogen-agents
Use when autoGen multi-agent conversations — AssistantAgent, UserProxyAgent, group chat, code execution. Use when working with autogen agents.
crewai-agents
Use when crewAI multi-agent orchestration — agents, tasks, crews, tools, memory, delegation. Use when working with crewai agents.
hs-release
Cut a core Hindsight release (vX.Y.Z) and open the changelog + blog PR. Use when asked to cut/start a release, bump the version, or publish a new Hindsight version.
hindsight-local
Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user).
handoff
Resume the most recent agent session for the current working directory, leading with any unanswered question. Use when the user says "where were we", "resume", "handoff", "pick up where I left off", or starts a session with no fresh context.
agentmemory-agents
How agentmemory wires into host coding agents via the connect command. Use when installing agentmemory into a specific agent, when asked which agents are supported, or when a connect adapter writes the wrong config path.