Individual skill
spec-mining
Apply this opinionated workflow when reverse-engineering legacy or undocumented systems: scope, explore the code, trace data flows, document in EARS format, flag uncertainties. For code archaeology, onboarding, and requirements extraction.
plugins/engineering/cortex-modernization/skills/spec-mining/SKILL.md
Source commit 8e809de409ce0c6539f0e31e821196efc4d7c7c9
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Instructions only
- Last checked
- Nov 5, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 18
- Repository last updated
- Oct 23, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-skill Repository topic
- ai-skills Repository topic
- claude-code Repository topic
- claude-code-skill Repository topic
- claude-code-skills Repository topic
- cursor Repository topic
- cursor-skill Repository topic
- cursor-skills Repository topic
- opencode-skill Repository topic
- opencode-skills Repository topic
- engineering Derived category
- operations Derived category
- data Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 3,109 bytesreferences/specification-template.mddoc · 2,180 bytes
Read excerpt
# Spec Mining
Extract a specification from a system that has no usable documentation, by reading the code that actually runs.
The discipline that makes the output trustworthy: **separate what you observed from what you inferred.** A reader will act on this document — rebuilding a service, planning a migration, onboarding — and a confident-sounding guess is worse than an acknowledged gap, because nobody goes back to check it.
## Approach
Work outside-in: entry points, then routes, then the services behind them, then the data layer. Following an actual request path teaches you the system's real structure, which is often not the structure its directory names advertise.
Read the tests too. They document intended behaviour and edge cases someone hit in production, and they are usually more honest than any comment or README in the repo.
Read the migration history as well. Migrations are dated and ordered, so they show how the schema arrived at its current shape — which columns were added under pressure, what was backfilled, what was renamed but never dropped. That sequence is often the only surviving record of why the data model looks the way it does.