Borrowing it
Nothing to install: this file belongs to shinpr/agentic-code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/shinpr/agentic-code/main/.agents/skills/metacognition/SKILL.mdgit clone --depth 1 https://github.com/shinpr/agentic-codeWrote 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/shinpr/agentic-code/metacognition)<a href="https://agentmods.dev/skills/shinpr/agentic-code/metacognition"><img src="https://agentmods.dev/badge/skills/shinpr/agentic-code/metacognition/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/shinpr/agentic-code/metacognition"><img src="https://agentmods.dev/badge/skills/shinpr/agentic-code/metacognition.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00040 | $0.00543 |
| Opus 5.5 | $0.00016 | $0.00217 |
| Sonnet 5.5 | $0.00008 | $0.00109 |
| Haiku 4.5 | $0.00004 | $0.00054 |
Grade A, and why
metacognition 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 2mo 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metacognition Protocol
Purpose
Prevent the agent from following an invalid route, expanding work without evidence, or declaring completion without observable proof.
Checkpoints
Run a checkpoint when:
- repository work begins;
- the task type or approved outcome changes;
- unexpected evidence invalidates the current approach;
- a major phase or coherent implementation task completes;
- the next action requires user authority or an irreversible operation.
Routine reversible actions inside an already validated approach do not require a separate checkpoint.
Assessment
Outcome and Scope
- What observable outcome owns this work?
- Which requirements are current, and which items are current-state evidence or speculation?
- What non-goals and contracts must remain unchanged?
- Could reuse or no-change already satisfy the outcome?
Evidence
- Which claims are observed, inferred, or unknown?
- What repository rule or pattern governs the next decision?
- Does an unknown change the outcome, scale, major design, authority, or verification?
Approach
- Is the current route the smallest sufficient one?
- Does every added artifact, mechanism, test, gate, or task change the outcome, protect a boundary, serve a consumer, or supply necessary proof?
- What current evidence would make the approach invalid?
Completion
- Is the requested result observable?
- Did the applicable established checks pass?
- Are verification gaps and residual limitations reported?
- Did review findings remain within approved scope?
Decision
After assessment, choose one:
- continue: Current evidence supports the route.
- adjust: A reversible local change keeps the approved outcome intact.
- return to owning phase: Evidence invalidates a requirement, major design decision, task boundary, or verification strategy.
- ask the user: Progress requires a new requirement, scope change, unavailable authority, or unauthorized irreversible action.
- complete: The outcome and required proof are observable.
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.
- 2mo ago First seen · 77 lines · 40 tokens per session scan A d4745f5b337d
metacognition is a skill published in the GitHub repository shinpr/agentic-code (50 stars, last pushed 7d ago), licensed MIT. It adds 40 tokens to every session and 543 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.
Other skills, from other repositories
agent-user-tools
Agent skill for user-tools - invoke with $agent-user-tools.
todos
Use when the user asks for a shared plan, a task needs at least three substantive execution steps, or a loose TODO is pending. Without an explicit plan request, not for one-shot answers or checks, or one- to two-step work that is not already in the list.
moai-factory-foreman
One unattended factory foreman iteration: watch the backlog queue, dispatch the next operator-picked card to an isolated worker, collect completion evidence on read (not on claims), and report. This is the body the project's loop.md driver invokes each iteration of a bare /loop; it can also be invoked directly to test…
context-preservation
State capture and restore across context window compactions. Monitors usage thresholds and serializes quality, task, and spec state for seamless continuation.
cog-braindump-capture
Capture raw thoughts with automatic domain classification and vault routing.
cog-knowledge-consolidation
Build structured knowledge frameworks from scattered vault notes with source attribution.