Speckit Analyze

Execution Steps

1. Initialize Analysis Context

Run .specify/scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks once from repo root and parse JSON for FEATURE_DIR and AVAILABLE_DOCS. Derive absolute paths:

• SPEC = FEATURE_DIR/spec.md
• PLAN = FEATURE_DIR/plan.md
• TASKS = FEATURE_DIR/tasks.md

Abort with an error message if any required file is missing (instruct the user to run missing prerequisite command). For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'''m Groot' (or double-quote if possible: "I'm Groot").

2. Load Artifacts (Progressive Disclosure)

Load only the minimal necessary context from each artifact:

From spec.md:

• Overview/Context
• Functional Requirements
• Success Criteria (measurable outcomes — e.g., performance, security, availability, user success, business impact)
• User Stories
• Edge Cases (if present)

From plan.md:

• Architecture/stack choices
• Data Model references
• Phases
• Technical constraints

From tasks.md:

• Task IDs
• Descriptions
• Phase grouping
• Parallel markers [P]
• Referenced file paths

From constitution:

• Load .specify/memory/constitution.md for principle validation

3. Build Semantic Models

Create internal representations (do not include raw artifacts in output):

• Requirements inventory: For each Functional Requirement (FR-###) and Success Criterion (SC-###), record a stable key. Use the explicit FR-/SC- identifier as the primary key when present, and optionally also derive an imperative-phrase slug for readability (e.g., "User can upload file" → user-can-upload-file). Include only Success Criteria items that require buildable work (e.g., load-testing infrastructure, security audit tooling), and exclude post-launch outcome metrics and business KPIs (e.g., "Reduce support tickets by 50%").
• User story/action inventory: Discrete user actions with acceptance criteria
• Task coverage mapping: Map each task to one or more requirements or stories (inference by keyword / explicit reference patterns like IDs or key phrases)
• Constitution rule set: Extract principle names and MUST/SHOULD normative statements

4. Detection Passes (Token-Efficient Analysis)

Focus on high-signal findings. Limit to 50 findings total; aggregate remainder in overflow summary.

A. Duplication Detection

• Identify near-duplicate requirements
• Mark lower-quality phrasing for consolidation

B. Ambiguity Detection

• Flag vague adjectives (fast, scalable, secure, intuitive, robust) lacking measurable criteria
• Flag unresolved placeholders (TODO, TKTK, ???, <placeholder>, etc.)

C. Underspecification

• Requirements with verbs but missing object or measurable outcome
• User stories missing acceptance criteria alignment
• Tasks referencing files or components not defined in spec/plan

D. Constitution Alignment

• Any requirement or plan element conflicting with a MUST principle
• Missing mandated sections or quality gates from constitution

E. Coverage Gaps

• Requirements with zero associated tasks
• Tasks with no mapped requirement/story
• Success Criteria requiring buildable work (performance, security, availability) not reflected in tasks

F. Inconsistency

• Terminology drift (same concept named differently across files)
• Data entities referenced in plan but absent in spec (or vice versa)
• Task ordering contradictions (e.g., integration tasks before foundational setup tasks without dependency note)
• Conflicting requirements (e.g., one requires Next.js while other specifies Vue)

5. Severity Assignment

Use this heuristic to prioritize findings:

• CRITICAL: Violates constitution MUST, missing core spec artifact, or requirement with zero coverage that blocks baseline functionality
• HIGH: Duplicate or conflicting requirement, ambiguous security/performance attribute, untestable acceptance criterion
• MEDIUM: Terminology drift, missing non-functional task coverage, underspecified edge case
• LOW: Style/wording improvements, minor redundancy not affecting execution order

6. Produce Compact Analysis Report

Output a Markdown report (no file writes) with the following structure:

Specification Analysis Report

(Add one row per finding; generate stable IDs prefixed by category initial.)

Coverage Summary Table:

Constitution Alignment Issues: (if any)

Unmapped Tasks: (if any)

Metrics:

• Total Requirements
• Total Tasks
• Coverage % (requirements with >=1 task)
• Ambiguity Count
• Duplication Count
• Critical Issues Count

7. Provide Next Actions

At end of report, output a concise Next Actions block:

• If CRITICAL issues exist: Recommend resolving before /speckit.implement
• If only LOW/MEDIUM: User may proceed, but provide improvement suggestions
• Provide explicit command suggestions: e.g., "Run /speckit.specify with refinement", "Run /speckit.plan to adjust architecture", "Manually edit tasks.md to add coverage for 'performance-metrics'"

8. Offer Remediation

Ask the user: "Would you like me to suggest concrete remediation edits for the top N issues?" (Do NOT apply them automatically.)

Operating Principles

Context Efficiency

• Minimal high-signal tokens: Focus on actionable findings, not exhaustive documentation
• Progressive disclosure: Load artifacts incrementally; don't dump all content into analysis
• Token-efficient output: Limit findings table to 50 rows; summarize overflow
• Deterministic results: Rerunning without changes should produce consistent IDs and counts

Analysis Guidelines

• NEVER modify files (this is read-only analysis)
• NEVER hallucinate missing sections (if absent, report them accurately)
• Prioritize constitution violations (these are always CRITICAL)
• Use examples over exhaustive rules (cite specific instances, not generic patterns)
• Report zero issues gracefully (emit success report with coverage statistics)

Context

$ARGUMENTS