By atscub
Metacognitive skills for epistemically humble, self-aware AI agents. Includes Socratic reasoning (with integrated steelmanning and falsification protocols), structured learning, post-task reflection, premortem risk analysis, problem reframing, first-principles decomposition, and coherence auditing.
Audit whether the parts of a system agree with each other and with reality. Use after building or modifying something with multiple components — documentation, code architecture, APIs, product messaging — where the parts must tell a consistent story. Trigger on: /meta:coherence, 'does this all make sense together', 'check for consistency', 'audit this', 'is this coherent', or when you've made changes across multiple files and need to verify they agree. Only for multi-component work where inconsistencies create compounding confusion. Skip for single-file changes.
Go to first principles when pattern-matching keeps failing on a novel problem. Use when analogies break, when inherited assumptions may be wrong, when a problem is too complex for surface-level reasoning, or when you need to understand *why* something works. Trigger on: /meta:decompose, 'break this down', 'first principles', 'why does this work', 'start from scratch'. Only for problems where surface-level approaches have demonstrably failed or where building on unexamined assumptions is risky.
Research a topic using current sources, learn how to do something new, then save the acquired knowledge as a reusable skill or memory. Use when: the user asks to learn a new technology/tool/pattern, needs to figure out how something works from scratch, says 'learn how to', 'figure out how to', 'research how to', or when you encounter a tool/API/framework you don't have reliable knowledge about. Only when the knowledge gap is significant and the task depends on getting it right. Skip for minor details you can test in seconds.
Anticipate failure before committing to a plan or implementation. Use before starting a risky implementation, making an architectural decision, deploying, or any high-stakes action. Trigger on: /meta:premortem, 'what could go wrong', 'risk check', 'before we do this', or when about to commit to an approach with significant consequences. Only for high-stakes implementations where failure is costly or hard to reverse. Skip for low-risk, easily reversible actions.
Analyze what went wrong and extract reusable lessons after recovering from a significant problem. Use when the agent hit a real obstacle, worked through it, and reached a solution — the goal is understanding the failure and preventing recurrence. Trigger on: /meta:reflect, 'what went wrong', 'what did we learn from that', 'retrospective', or after recovering from significant debugging or implementation struggles. Not for routine successes — only when something actually broke and was fixed. Only for significant work where the lessons have reuse value.
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A Claude Code plugin marketplace with two plugins: metacognition (thinking about thinking) and operations (routine engineering practices).
Humans aren't naturally good thinkers. We anchor on first impressions, dismiss ideas we don't like, stop at the first plausible answer, and confuse what we think we know with what we've actually verified. Left to pure instinct, we'd fall into these traps constantly — arguably more often than AI does.
What makes humans effective isn't raw thinking ability. It's metacognition — the capacity to think about how we think. Over centuries, we've built systems for this: the Socratic method, premortems, steelmanning, first-principles reasoning, structured reflection. These aren't innate talents. They're learned disciplines that compensate for the biases we're born with.
AI agents have the same underlying problem. They're capable reasoners that default to pattern-matching, and they have no built-in systems to catch when that goes wrong. The metacognition plugin gives them those systems — the same ones humans developed to become better thinkers.
Similarly, software engineering has routine practices that experienced engineers follow without thinking: check your work before walking away, write PR descriptions that reviewers can follow, hand off context when someone else picks up the work, summarize changes for the people who need to know. These aren't creative acts — they're disciplines. The operations plugin formalizes them so the agent follows them consistently.
KYL includes two independent plugins — install either or both. See each plugin's README for the full skill list.
| Plugin | What it does | Skills |
|---|---|---|
| Metacognition | Thinking about thinking — bias awareness, research, reflection, risk assessment, first-principles reasoning, coherence auditing. | 7 skills |
| Operations | Routine engineering practices — PR lifecycle, sanity checks, handoffs, changelogs, workflow coordination. | 8 skills |
Add the marketplace to your Claude Code settings:
# In Claude Code
/plugin marketplace add atscub/know-your-limits
/plugin install metacognition
/plugin install operations
# Restart Claude Code
Or manually in ~/.claude/settings.json:
{
"extraKnownMarketplaces": {
"know-your-limits": {
"source": {
"source": "github",
"repo": "atscub/know-your-limits"
}
}
},
"enabledPlugins": {
"metacognition": true,
"operations": true
}
}
You can install either plugin independently — they don't depend on each other.
Contributions are welcome but will be accepted discretionally. I will try to be unbiased, therefore changes are more likely to be accepted if they are backed up, ideally with data.
This is experimental and provided as-is. It modifies AI agent behavior by injecting metacognitive prompts — results may vary across models, tasks, and contexts. The skills are heuristic, not guarantees: they can improve reasoning quality but do not eliminate errors, hallucinations, or other AI limitations. Use your own judgment when acting on AI-generated analysis, and always verify critical decisions independently. If you burn all your tokens, don't yell at me.
MIT
npx claudepluginhub atscub/know-your-limits --plugin metacognitionDay-to-day engineering operations skills for AI agents. PR workflows, code review, handoffs, changelogs, playbook authoring, and process orchestration.
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