aggregate-logs
Generate LEARNINGS.md from skill execution logs over a configurable time window.
Create or update the feature specification from a natural language feature description.
> /plugin marketplace add athola/claude-night-marketHow it fires
How this command gets triggered: by you, by Claude, or both.
/speckit-specifyContext preview
What this command does when you run it.
Create or update the feature specification from a natural language feature description.
description: Create or update the feature specification from a natural language feature description. argument-hint: "Feature description or requirements URL"
$ARGUMENTS
You **MUST** consider the user input before proceeding (if not empty).
The text the user typed after `/speckit-specify` in the triggering message **is** the feature description. Assume you always have it available in this conversation even if `$ARGUMENTS` appears literally below. Do not ask the user to repeat it unless they provided an empty command.
Given that feature description, do this:
1. **Generate a concise short name** (2-4 words) for the branch:
2. **Check for existing branches before creating new one**:
a. First, fetch all remote branches to validate we have the latest information:
git fetch --all --prune
b. Find the highest feature number across all sources for the short-name:
c. Determine the next available number:
d. Run the script `.specify/scripts/bash/create-new-feature.sh --json "$ARGUMENTS"` with the calculated number and short-name:
**IMPORTANT**:
3. Load `.specify/templates/spec-template.md` to understand required sections.
4. Follow this execution flow:
1. Parse user description from Input If empty: ERROR "No feature description provided" 2. Extract key concepts from description Identify: actors, actions, data, constraints 3. For unclear aspects:
4. Fill User Scenarios & Testing section If no clear user flow: ERROR "Cannot determine user scenarios" 5. Generate Functional Requirements Each requirement must be testable Use reasonable defaults for unspecified details (document assumptions in Assumptions section) 6. Define Success Criteria Create measurable, technology-agnostic outcomes Include both quantitative metrics (time, performance, volume) and qualitative measures (user satisfaction, task completion) Each criterion must be verifiable without implementation details 7. Identify Key Entities (if data involved) 8. Return: SUCCESS (spec ready for planning)
5. Write the specification to SPEC_FILE using the template structure, replacing placeholders with concrete details derived from the feature description (arguments) while preserving section order and headings.
6. **Specification Quality Validation**: After writing the initial spec, validate it against quality criteria:
a. **Create Spec Quality Checklist**: Generate a checklist file at `FEATURE_DIR/checklists/requirements.md` using the checklist template structure with these validation items:
# Specification Quality Checklist: [FEATURE NAME]
**Purpose**: Validate specification completeness and quality before proceeding to planning
**Created**: [DATE]
**Feature**: [Link to spec.md]
## Content Quality
- [ ] No implementation details (languages, frameworks, APIs)
- [ ] Focused on user value and business needs
- [ ] Written for non-technical stakeholders
- [ ] All mandatory sections completed
## Requirement Completeness
- [ ] No [NEEDS CLARIFICATION] markers remain
- [ ] Requirements are testable and unambiguous
- [ ] Success criteria are measurable
- [ ] Success criteria areA plugin marketplace for Claude Code. Install only the plugins you need to run git workflows, code review, spec-driven development, and autonomous agents from inside your Claude Code session.
Generate LEARNINGS.md from skill execution logs over a configurable time window.
Analyze skill file complexity metrics and generate modularization recommendations for splitting or progressive loading.
Scaffold new Claude Code skills with brainstorming, TDD methodology, and proper frontmatter and module structure.