القائمة
Metacognitive know/don't-know assessment before deep research or complex implementation. Surfaces knowledge gaps to prevent wasted exploration cycles.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Scan for stale memory topics, near-dead instincts, and low-salience K-LEAN entries. Proposes cleanup actions interactively.
Quality gate for instinct persistence. Validates new instincts through a 4-stage pipeline before allowing them to be written to disk. Invoke before writing any new instinct file.
Use when the user needs thorough research on a complex topic, says 'deep dive', 'research this thoroughly', or 'sensor sweep'. Parallel agent orchestrator for comprehensive multi-angle investigation.
Autonomous experiment loop: iteratively improves a single file against a measurable metric. Uses git-as-state-management in a disposable worktree. Invoke when user wants to optimize, tune, or systematically improve a file through repeated iterations.
Use when you need multi-faction review combining security, strategy, cost, and quality perspectives. Trigger: fleet review, multi-faction assessment, comprehensive quality gate, or when escalation requires L5+.
Two-loop research orchestrator: chains deep-research (outer, hypothesis generation) with experiment-loop (inner, empirical testing). Use when research questions have a testable dimension with a measurable metric.
| name | confidence |
| description | Metacognitive know/don't-know assessment before deep research or complex implementation. Surfaces knowledge gaps to prevent wasted exploration cycles. |
Structured assessment of what you know vs. what you're assuming vs. what you don't know about the current task. Run before /deep-research, complex multi-file implementation, or any task where wrong assumptions could waste a phase.
State the task in one sentence. Then list the 3-5 key technical questions the task depends on.
For each question, classify into exactly one category:
| Question | State | Evidence | Gap-Closing Action |
|----------|-------|----------|-------------------|
| [question] | KNOWN | [file:line, doc URL, or K-LEAN entry] | - |
| [question] | ASSUMED | [reasoning basis, no hard evidence] | [verify how] |
| [question] | UNKNOWN | - | [research action needed] |
Rules:
Confidence Score: [KNOWN count] / [total questions] = [percentage]%
Risk Level: HIGH (< 50%) | MEDIUM (50-75%) | LOW (> 75%)
If Risk Level is HIGH:
If Risk Level is MEDIUM:
If Risk Level is LOW:
Present the assessment table + risk level, then ask:
Confidence: [X/Y] ([percentage]%)
Risk: [HIGH/MEDIUM/LOW]
Unknowns: [count] requiring action
Assumptions: [count] requiring verification
Proceed? [Y / research unknowns first / verify assumptions first]
From Compound Knowledge Plugin fleet review (2026-03-24): /kw:confidence is a genuine gap in our setup. We have no explicit "what do I know vs. what don't I know" assessment. Instincts encode past knowledge; fleet reviews evaluate output; but nothing surfaces uncertainty about the CURRENT task in real time. This prevents research rabbit holes (exploring what you already know) and implementation failures (building on unverified assumptions). Ferengi ROI: 5-minute assessment prevents 30-60 minutes of wasted exploration.