critique
On-demand multi-model critic consensus for arbitrary concerns
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On-demand multi-model critic consensus for arbitrary concerns
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
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Generate structured implementation plans for vertical slices of working software with comprehensive research, codebase context, and validation gates
Perform thorough, structured reviews of engineering implementation plans with claim verification, research validation, and risk assessment
Multi-model critic consensus PR reviews -- bugs only, false-positive filtered, with structured severity and go/no-go verdicts
| name | critique |
| description | On-demand multi-model critic consensus for arbitrary concerns |
Evaluate user-provided concerns through multi-model critic consensus. Takes one or more concerns (about code, architecture, design, process, or any artifact), investigates each via the researcher agent, then filters through multi-model critic consensus to separate real issues from speculative or unfounded worries.
Goal: "Is this concern real, evidenced, and significant -- or speculative noise?"
If SKIP_CRITIQUE=true or SKIP_CRITIQUE=1 environment flag is set, then skip all steps and exit immediately with a message: "Critique skill skipped due to SKIP_CRITIQUE flag."
env | grep -i "SKIP_CRITIQUE"
# or
cat ~/.env | grep -i "SKIP_CRITIQUE"
Extract individual concerns from user input. Each concern should have:
If the user provides a single block of text with multiple concerns, split them into individual items. If boundaries are ambiguous, ask the user to clarify.
For each concern, invoke the researcher agent using spawn:
spawn(agent: "researcher", task: "<concern description + context references + codebase path>")
The researcher investigates against actual source material and returns findings with confidence levels (CERTAIN/LIKELY/POSSIBLE/DECLINE) and verbatim evidence.
Only concerns with CERTAIN or LIKELY confidence proceed to Step 3. Concerns rated POSSIBLE or DECLINE are reported in the output as "Insufficient Evidence" with the researcher's notes -- they skip critic evaluation.
If the researcher fails or times out, fall back to inline verification: read the referenced files, trace dependencies, and verify claims directly.
Invoke multi-model critic consensus:
~/.agents/skills/critique/critics.yml to get the available critic models~/.agents/skills/critique/critic-prompt.md to get the shared evaluation promptspawn call with tasks array -- one task per critic model.
task field = the critic prompt + concern + evidence + evaluation criteria below.model field = the model identifier from critics.yml.KEEP, REJECT, or ABSTAIN as the first response line.REJECT if any of these apply:
KEEP only if ALL true:
Write structured analysis to ./tmp/critics/<label>-analysis.md.
The label is derived from user input:
YYYYMMDD-HHMMSS-analysis.md suffix) at 128 characters totalUse this output template:
# Concerns Analysis: <label>
**Date:** <date> | **Concerns:** <total> | **Validated:** <survived count> | **Filtered:** <filtered count> | **Insufficient Evidence:** <skipped count>
## Summary
<2-3 sentences: what was analyzed, consensus outcome, cost stats>
## Validated Concerns
### <Concern title>
- **Description:** <what the concern is about>
- **Evidence:** <researcher findings with file:line references>
- **Impact:** <concrete impact assessment>
- **Critic consensus:** <votes>/<critics> KEEP
- **Recommendation:** <suggested action>
## Filtered Concerns
<N> concerns filtered by critic consensus:
- <title> (votes: <votes>/<critics> KEEP -- <primary rejection reason>)
## Researcher Notes
[Optional -- include when researcher investigation surfaces valuable factual findings separate from the concern's validity. Omit this section if no additional findings emerged.]
## Insufficient Evidence
<N> concerns skipped (researcher confidence below LIKELY):
- <title> -- <researcher notes on why evidence was insufficient>
Omit empty sections. Copy to clipboard if pbcopy/xclip available.
After execution, use skill-improver to capture observations. Before execution, check SKILL_NOTES.md for known edge cases.