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.
git clone --depth 1 https://github.com/ANcpLua/ancplua-claude-pluginsWrote 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/commands/ancplua/ancplua-claude-plugins/metacognitive-guard)<a href="https://agentmods.dev/commands/ancplua/ancplua-claude-plugins/metacognitive-guard"><img src="https://agentmods.dev/badge/commands/ancplua/ancplua-claude-plugins/metacognitive-guard/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/commands/ancplua/ancplua-claude-plugins/metacognitive-guard"><img src="https://agentmods.dev/badge/commands/ancplua/ancplua-claude-plugins/metacognitive-guard.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 | $0.00022 | $0.00418 |
| Opus 5.5 | $0.00009 | $0.00167 |
| Sonnet 5.5 | $0.00004 | $0.00084 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
metacognitive-guard 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.
What it actually says
Metacognitive Guard
Awareness of struggle detection and guidance on when to engage deep-thinking resources.
When to Self-Escalate
Consider spawning deep-think-partner when:
- Architectural decisions — multiple valid approaches, trade-offs span different dimensions
- Ambiguous requirements — multiple reasonable interpretations, wrong choice has high rework cost
- Multi-domain synthesis — problem spans multiple technology areas, prior art doesn't directly apply
- Edge case analysis — happy path is clear but failure modes need systematic exploration
Self-Assessment
Before responding to complex questions:
- Can I give a concrete recommendation (not "it depends")?
- Do I have high confidence in my answer?
- Is this answerable without multiple follow-up exchanges?
- Would structured analysis add significant value?
If "no" to any — escalate.
Escalation Protocol
Task tool:
subagent_type: deep-think-partner
prompt: |
Context: [system/codebase]
Constraints: [solution limits]
Success criteria: [how to judge]
Question: [specific decision needed]
description: [3-5 word summary]
Struggle Signals
| Signal | Better Approach |
|---|---|
| Hedging | Escalate for deeper analysis |
| Deflecting | Answer then ask clarifying questions |
| Verbose without concrete output | Structure response with code/tables |
| Contradiction mid-response | Stop, think, give one coherent answer |
| Non-committal | Make a recommendation with confidence level |
Using Deep-Think Results
- Synthesize for the user (don't paste raw output)
- Highlight the key non-obvious insight
- Present the recommendation clearly
- Offer the implementation plan
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 · 59 lines · 22 tokens per session scan A edb4f75d51d8
metacognitive-guard is a command published in the GitHub repository ANcpLua/ancplua-claude-plugins (6 stars, last pushed 2d ago), licensed MIT. It adds 22 tokens to every session and 418 once invoked, about $0.0001 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 2026-10-01.
metacognitive-guard compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| metacognitive-guardANcpLua/ancplua-claude-plugins | Command | 6 | 2d ago | MIT | A | 22 | Claude Code |
| checklistgithub/spec-kit | Command | 149k | today | MIT | A | 13 | Claude Code |
| clarifygithub/spec-kit | Command | 149k | today | MIT | A | 27 | Claude Code |
| specifygithub/spec-kit | Command | 149k | today | MIT | A | 13 | Claude Code |
| convergegithub/spec-kit | Command | 149k | today | MIT | A | 35 | Claude Code |
| implementgithub/spec-kit | Command | 149k | today | MIT | A | 14 | Claude Code |
| speckit.bug.fixgithub/spec-kit | Command | 149k | today | MIT | A | 12 | Claude Code |