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 jpoehnelt/skills --skill agents-reflectgit clone --depth 1 https://github.com/jpoehnelt/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/jpoehnelt/skills/agents-reflect)<a href="https://agentmods.dev/skills/jpoehnelt/skills/agents-reflect"><img src="https://agentmods.dev/badge/skills/jpoehnelt/skills/agents-reflect/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/jpoehnelt/skills/agents-reflect"><img src="https://agentmods.dev/badge/skills/jpoehnelt/skills/agents-reflect.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 | $0.00047 | $0.00846 |
| Opus 5.5 | $0.00019 | $0.00338 |
| Sonnet 5.5 | $0.00009 | $0.00169 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
agents-reflect 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 8d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect
Mine the current conversation for durable lessons, then route each into an edit. Use the conversation already in context. Do not read transcript files.
Skip when the session was trivial or a skill the agent followed correctly already covers it. One-offs are not lessons.
Repo facts (from AGENTS.md or CLAUDE.md): the skills folder and table, where durable invariants live, where lint or syntactic rules live, and where design docs or plans belong.
1. Collect evidence
List, from this conversation:
- Corrections the user made to the agent.
- Steps that failed, were retried, or were redone.
- Searches that took too long, and the fact that would have shortened them.
- Things that worked and should be repeated.
- Instructions the agent ignored or misread.
Each item needs a concrete quote or event from the conversation. Drop anything without one.
2. Review through three lenses
When the user authorizes delegation, spawn three parallel subagents in one turn, using a smaller, faster model. Otherwise, review the three lenses yourself. (Claude Code: the Agent tool; Codex: its subagent/collaboration tool.) If the client caps concurrent subagents, run the lenses in batches within that cap. Paste the evidence list into each brief. Each lens returns findings with the supporting evidence.
- Judgment. Wrong decisions, missed questions, unverified claims, scope creep.
- Tooling. Wrong commands, missing scripts, slow paths, checks that should have been automated.
- Divergent. What a different approach would have avoided, and which assumption was wrong.
Synthesize the findings yourself, or use one synthesizer subagent on the strongest available model when delegation is authorized. It gets all three outputs, merges duplicates, and returns Accepted, Rejected, and Backlog lists.
3. Route each lesson
Find where it belongs. Run ls .agents/skills and read AGENTS.md and its skills table, if one exists. Follow any placement rule AGENTS.md states.
- A procedure or design check in an existing skill's area: edit that
.agents/skills/<name>/SKILL.md, usually its review checklist. Keep it within 80 lines, cutting text to make room. If the repo vendors that skill from a shared source, propose the change upstream instead of editing the copy. - A durable invariant, not a procedure: edit one line of
AGENTS.md. - Principles, decision records, and plans go where AGENTS.md says they live, not into a new docs file.
- No skill fits and the lesson will recur: propose a new skill folder and add it to the skills table.
- A rule a lint, script, or type could enforce: move it to Backlog and propose the enforcer (in the repo's existing lint or check system) instead of more text. The
agents-correctskill covers the ladder.
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.
- 8d ago Changed · -1 lines 9b17d77844ca
- 9d ago First seen · 66 lines · 47 tokens per session scan A 1541253679d5
agents-reflect is a skill published in the GitHub repository jpoehnelt/skills (23 stars, last pushed 3d ago), licensed MIT. It adds 47 tokens to every session and 846 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 2026-10-21.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…