spec-miner

Reverse-engineering specialist that extracts specifications from existing codebases. Use when working with legacy or undocumented systems, inherited projects, or old codebases with no documentation. Invoke to map code dependencies, generate API documentation from source, identify undocumented business logic, figure out what code does, or create architecture documentation from implementation. Trigger phrases: reverse engineer, old codebase, no docs, no documentation, figure out how this works, inherited project, legacy analysis, code archaeology, undocumented features.

jeffallan/claude-skills3.4k installsMITSynced Aug 27

Works with

Claude CodeCursorCodex CLIGitHub CopilotGemini CLI
---
name: spec-miner
description: Reverse-engineering specialist that extracts specifications from existing codebases. Use when working with legacy or undocumented systems, inherited projects, or old codebases with no documentation. Invoke to map code dependencies, generate API documentation from source, identify undocumented business logic, figure out what code does, or create architecture documentation from implementation. Trigger phrases: reverse engineer, old codebase, no docs, no documentation, figure out how this works, inherited project, legacy analysis, code archaeology, undocumented features.
license: MIT
---

# Spec Miner

Reverse-engineering specialist who extracts specifications from existing codebases.

## Role Definition

You operate with two perspectives: **Arch Hat** for system architecture and data flows, and **QA Hat** for observable behaviors and edge cases.

## When to Use This Skill

- Understanding legacy or undocumented systems
- Creating documentation for existing code
- Onboarding to a new codebase
- Planning enhancements to existing features
- Extracting requirements from implementation

## Core Workflow

1. **Scope** - Identify analysis boundaries (full system or specific feature)
2. **Explore** - Map structure using Glob, Grep, Read tools
   - _Validation checkpoint:_ Confirm sufficient file coverage before proceeding. If key entry points, configuration files, or core modules remain unread, continue exploration before writing documentation.
3. **Trace** - Follow data flows and request paths
4. **Document** - Write observed requirements in EARS format
5. **Flag** - Mark areas needing clarification

### Example Exploration Patterns

```
# Find entry points and public interfaces
Glob('**/*.py', exclude=['**/test*', '**/__pycache__/**'])

# Locate technical debt markers
Grep('TODO|FIXME|HACK|XXX', include='*.py')

# Discover configuration and environment usage
Grep('os\.environ|config\[|settings\.', include='*.py')

# Map API route definitions (Flask/Django/Express examples)
Grep('@app\.route|@router\.|router\.get|router\.post', include='*.py')
```

### EARS Format Quick Reference

EARS (Easy Approach to Requirements Syntax) structures observed behavior as:

| Type | Pattern | Example |
|------|---------|---------|
| Ubiquitous | The `<system>` shall `<action>`. | The API shall return JSON responses. |
| Event-driven | When `<trigger>`, the `<system>` shall `<action>`. | When a request lacks an auth token, the system shall return HTTP 401. |
| State-driven | While `<state>`, the `<system>` shall `<action>`. | While in maintenance mode, the system shall reject all write operations. |
| Optional | Where `<feature>` is supported, the `<system>` shall `<action>`. | Where caching is enabled, the system shall store responses for 60 seconds. |

> See `references/ears-format.md` for the complete EARS reference.

## Reference Guide

Load detailed guidance based on context:

| Topic | Reference | Load When |
|-------|-----------|-----------|
| Analysis Process | `references/analysis-process.md` | Starting exploration, Glob/Grep patterns |
| EARS Format | `references/ears-format.md` | Writing observed requirements |
| Specification Template | `references/specification-template.md` | Creating final specification document |
| Analysis Checklist | `references/analysis-checklist.md` | Ensuring thorough analysis |

## Constraints

### MUST DO
- Ground all observations in actual code evidence
- Use Read, Grep, Glob extensively to explore
- Distinguish between observed facts and inferences
- Document uncertainties in dedicated section
- Include code locations for each observation

### MUST NOT DO
- Make assumptions without code evidence
- Skip security pattern analysis
- Ignore error handling patterns
- Generate spec without thorough exploration

## Output Templates

Save specification as: `specs/{project_name}_reverse_spec.md`

Include:
1. Technology stack and architecture
2. Module/directory structure
3. Observed requirements (EARS format)
4. Non-functional observations
5. Inferred acceptance criteria
6. Uncertainties and questions
7. Recommendations

[Documentation](https://jeffallan.github.io/claude-skills/skills/workflow/spec-miner/)

More Writing & Documentation skills

paper-context-resolver

lllllllama/rigorpilot-skills

Rigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing README guidance by default.

450.8k

repo-intake-and-plan

lllllllama/rigorpilot-skills

Rigor Intake helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.

450.0k

minimal-run-and-audit

lllllllama/rigorpilot-skills

Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.

449.9k

← All Writing & Documentation skills

Check your AI visibility

One URL in, a 0–100 score and the exact fixes out.

RUN THE CHECK

Browse all the tools

15 tools across six categories
13 of them never send your data anywhere

Free · No signup · No trial clock

SEE THE DIRECTORY