LQF_Machine_Learning_E…
LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine…
Generate custom quality checklists for validating requirements completeness
Claude Code
$ npx -y skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-checklist --agent claude-code
Codex
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-checklist -a codex -y
Cursor
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-checklist -a cursor -y
Gemini CLI
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-checklist -a gemini-cli -y
GitHub Copilot
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-checklist -a github-copilot -y
OpenCode
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-checklist -a opencode -y
Windsurf
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-checklist -a windsurf -y
name: speckit-checklist description: Generate custom quality checklists for validating requirements completeness and clarity. Use to create unit tests for English that ensure spec quality before implementation. compatibility: Requires spec-kit project structure with .specify/ directory metadata: author: github-spec-kit source: templates/commands/checklist.md
**CRITICAL CONCEPT**: Checklists are **UNIT TESTS FOR REQUIREMENTS WRITING** - they validate the quality, clarity, and completeness of requirements in a given domain.
**NOT for verification/testing**:
**FOR requirements quality validation**:
**Metaphor**: If your spec is code written in English, the checklist is its unit test suite. You're testing whether the requirements are well-written, complete, unambiguous, and ready for implementation - NOT whether the implementation works.
$ARGUMENTS
You **MUST** consider the user input before proceeding (if not empty).
1. **Setup**: Run `.specify/scripts/powershell/check-prerequisites.ps1 -Json` from repo root and parse JSON for FEATURE_DIR and AVAILABLE_DOCS list.
2. **Clarify intent (dynamic)**: Derive up to THREE initial contextual clarifying questions (no pre-baked catalog). They MUST:
Generation algorithm: 1. Extract signals: feature domain keywords (e.g., auth, latency, UX, API), risk indicators ("critical", "must", "compliance"), stakeholder hints ("QA", "review", "security team"), and explicit deliverables ("a11y", "rollback", "contracts"). 2. Cluster signals into candidate focus areas (max 4) ranked by relevance. 3. Identify probable audience & timing (author, reviewer, QA, release) if not explicit. 4. Detect missing dimensions: scope breadth, depth/rigor, risk emphasis, exclusion boundaries, measurable acceptance criteria. 5. Formulate questions chosen from these archetypes:
Question formatting rules:
Defaults when interaction impossible:
Output the questions (label Q1/Q2/Q3). After answers: if ≥2 scenario classes (Alternate / Exception / Recovery / Non-Functional domain) remain unclear, you MAY ask up to TWO more targeted follow‑ups (Q4/Q5) with a one-line justification each (e.g., "Unresolved recovery path risk"). Do not exceed five total questions. Skip escalation if user explicitly declines more.
3. **Understand user request**: Combine `$ARGUMENTS` + clarifying answers:
4. **Load feature context**: Read from FEATURE_DIR:
**Context Loading Strategy**:
5. **Generate checklist** - Create "Unit Tests for Requirements":
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