speckit-baseline

• You need a spec for existing or legacy code.
• You want to document a feature before refactoring.
• You inherited a codebase without written requirements.

• A target path, file list, or glob pattern describing the code to analyze.
• Repo context with .specify/ scripts and templates.

If the target is missing or ambiguous, ask a focused question before continuing.

Generate a technology-agnostic spec for existing code, then create the feature branch/spec file using the standard Spec Kit templates.

1. Parse target input: Identify files, directories, or patterns to analyze.

- Accept file paths, glob patterns, or directory paths.

- If empty: stop and ask for a concrete target.

1. Discover and read source files:

- Expand globs to a file list.

- Read file contents for analysis.

- Identify primary language(s) and frameworks.

- Map key file relationships and dependencies.

1. Analyze code structure:

- Identify entry points and public interfaces.

- Extract function/method signatures and behaviors.

- Find data models and entities.

- Detect API endpoints and routes.

- Identify user-facing functionality.

1. Generate a short name (2-4 words) from the analyzed code:

- Use action-noun format (e.g., "user-auth", "payment-processing").

- Base on primary functionality discovered.

- Preserve technical terms where meaningful.

1. Create the feature branch and spec file:

- Find the highest existing feature number for this short name (branches/specs).

- Run .specify/scripts/bash/create-new-feature.sh --json with the calculated number and short name.

- Read BRANCH_NAME, FEATURE_DIR, and SPEC_FILE paths from the script JSON output.

- 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").

1. Load the spec template from .specify/templates/spec-template.md.

1. Draft the specification using the template structure:

- User Stories: Infer from user-facing code paths and interactions.

- Acceptance Scenarios: Derive from validation logic, error handling, and tests.

- Functional Requirements: Extract from business rules and constraints.

- Key Entities: Identify from data models and schemas.

- Success Criteria: Infer from metrics, logging, or performance-related code.

- Assumptions: Document inferences made during analysis.

1. Abstract implementation details:

- Convert technical patterns to user-focused requirements.

- Remove framework-specific terminology.

- Focus on WHAT the code does, not HOW it does it.

1. Create spec quality checklist at FEATURE_DIR/checklists/requirements.md.

1. Report completion with:

- Branch name and spec file path.

- Summary of analyzed files.

- Key features discovered.

- Areas needing clarification or review.

• specs//spec.md
• specs//checklists/requirements.md

• Focus on extracting WHAT and WHY from HOW.
• Abstract away implementation details in the generated spec.
• Document assumptions made during code analysis.
• Flag areas where code behavior is unclear.
• Preserve discovered business rules and constraints.
• Use [NEEDS CLARIFICATION] for ambiguous code sections (max 3).
• Generated specs should be validated by someone who knows the feature.

Code Pattern → Spec Requirement:

• if (user.role === 'admin') → "System MUST restrict action to administrator users"
• password.length >= 8 → "Passwords MUST be at least 8 characters"
• cache.set(key, value, 3600) → "System MUST cache results for improved performance"
• try { ... } catch (e) { notify(e) } → "System MUST notify users when errors occur"

Code Pattern → User Story:

• Login endpoint with OAuth → "As a user, I can sign in using my social account"
• Shopping cart logic → "As a customer, I can add items to my cart for later purchase"
• Report generation → "As an analyst, I can generate reports on system activity"

After generating spec.md:

• Clarify with domain experts using speckit-clarify.
• Plan modernization/refactoring with speckit-plan.
• Compare the generated spec with actual requirements to identify gaps.