LQF_Machine_Learning_E…
LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine…
Structured clarification workflow for underspecified requirements. Use
Claude Code
$ npx -y skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-clarify --agent claude-code
Codex
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-clarify -a codex -y
Cursor
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-clarify -a cursor -y
Gemini CLI
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-clarify -a gemini-cli -y
GitHub Copilot
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-clarify -a github-copilot -y
OpenCode
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-clarify -a opencode -y
Windsurf
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill speckit-clarify -a windsurf -y
name: speckit-clarify description: Structured clarification workflow for underspecified requirements. Use before planning to resolve ambiguities through coverage-based questioning. Records answers in spec clarifications section. compatibility: Requires spec-kit project structure with .specify/ directory metadata: author: github-spec-kit source: templates/commands/clarify.md
$ARGUMENTS
You **MUST** consider the user input before proceeding (if not empty).
Goal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file.
Note: This clarification workflow is expected to run (and be completed) BEFORE invoking `/speckit.plan`. If the user explicitly states they are skipping clarification (e.g., exploratory spike), you may proceed, but must warn that downstream rework risk increases.
Execution steps:
1. Run `.specify/scripts/powershell/check-prerequisites.ps1 -Json -PathsOnly` from repo root **once** (combined `--json --paths-only` mode / `-Json -PathsOnly`). Parse minimal JSON payload fields:
2. Load the current spec file. Perform a structured ambiguity & coverage scan using this taxonomy. For each category, mark status: Clear / Partial / Missing. Produce an internal coverage map used for prioritization (do not output raw map unless no questions will be asked).
Functional Scope & Behavior:
Domain & Data Model:
Interaction & UX Flow:
Non-Functional Quality Attributes:
Integration & External Dependencies:
Edge Cases & Failure Handling:
Constraints & Tradeoffs:
Terminology & Consistency:
Completion Signals:
Misc / Placeholders:
For each category with Partial or Missing status, add a candidate question opportunity unless:
3. Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:
4. Sequential questioning loop (interactive):
| Option | Description | |--------|-------------| | A | <Option A description> | | B | <Option B description> | | C | <Option C description> (add D/E as needed up to 5) | | Short | Provide a different short answer (<=5 words) (Include only if free-form alternative is appropriate) |
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LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine…
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