speckit-baseline: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

Generate feature specifications by analyzing existing source code.

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Spec Kit Baseline Skill

When to Use

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

Inputs

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.

Goal

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

Workflow

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.
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.
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.
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.
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").
Load the spec template from .specify/templates/spec-template.md.
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.
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.
Create spec quality checklist at FEATURE_DIR/checklists/requirements.md.
Report completion with:
- Branch name and spec file path.
- Summary of analyzed files.
- Key features discovered.
- Areas needing clarification or review.

Outputs

specs/<feature>/spec.md
specs/<feature>/checklists/requirements.md

Key rules

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.

Examples

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"

Next Steps

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.

View source ↗

File metadata

name: speckit-baseline
description: Generate feature specifications by analyzing existing source code.

View original text

---
name: speckit-baseline
description: Generate feature specifications by analyzing existing source code.
---

# Spec Kit Baseline Skill

## When to Use

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

## Inputs

- 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.

## Goal

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

## Workflow

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.

2. **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.

3. **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.

4. **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.

5. **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").

6. **Load the spec template** from `.specify/templates/spec-template.md`.

7. **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.

8. **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.

9. **Create spec quality checklist** at `FEATURE_DIR/checklists/requirements.md`.

10. **Report completion** with:
- Branch name and spec file path.
- Summary of analyzed files.
- Key features discovered.
- Areas needing clarification or review.

## Outputs

- `specs/<feature>/spec.md`
- `specs/<feature>/checklists/requirements.md`

## Key rules

- 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.

## Examples

**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"

## Next Steps

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.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: AGPL-3.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 129 stars, 26 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: dceoy/speckit-agent-skills
License: AGPL-3.0
Version: 1.0.0
Last GitHub push: Aug 26, 2026
Registry updated: Sep 4, 2026
Instruction path: skills/speckit-baseline/SKILL.md @ d299afab0882

Version reported in registry metadata; check source releases before relying on it.

Quality

68/100

Promising

Trust

63/100

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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Source: dceoy/speckit-agent-skills
Indexed by: OpenAgentSkill community index

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