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    Code Archaeologist — Bundle

    by GarphenGate

    1

    A legacy systems specialist that maps unfamiliar codebases using evidence-driven analysis and historical context.

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    See it in action

    You say

    I just inherited this legacy Node.js repo. I need to understand how the auth flow works before I try to add MFA. Can you map it out?

    Your agent does

    Map first, dig second. I've identified the core logic in src/middleware/auth.js (lines 22-85). It appears to use a custom JWT implementation. I need to read src/services/token-vault.js to confirm how secrets are rotated before we touch the flow.

    What you get

    Map dependencies in undocumented legacy monoliths.Reconstruct original developer intent using git history and source analysis.Identify high-risk areas before starting a migration.Onboard engineers to acquired repos without breaking existing logic.

    About this skill

    The problem

    Inheriting an undocumented legacy codebase or a repo from an acquired startup often leads to weeks of confusion. Jumping straight into refactoring without understanding the hidden dependencies and historical intent results in breaking changes and technical debt.

    What it does

    • Surveys unfamiliar codebases to map structure and dependencies before suggesting modifications.
    • Cites specific file paths and line numbers for every claim to ensure evidence-based analysis.
    • Categorizes findings into confidence tiers: confirmed facts, probable theories, and speculative inferences.
    • Investigates version control logs and blame data to reconstruct the original intent behind complex logic.
    • Recommends the smallest safe next step for onboarding rather than high-risk, large-scale rewrites.

    Why this beats prompting it yourself

    Generic LLMs often hallucinate structural details or suggest aggressive, destructive refactors of code they don't fully grasp. This skill enforces a read-only, evidence-first methodology that prevents premature code changes and ensures every observation is grounded in the actual source files.

    Use cases

    • Onboarding to a legacy monolith where the original authors are no longer available.
    • Audit acquired repositories to understand architectural patterns and hidden risks.
    • Mapping dependencies before starting a migration or major feature update.
    • Documenting tribal knowledge by analyzing commit history and code patterns.

    Known limitations

    Does not provide legal or license interpretations. Refuses to perform deletions or refactors during the initial survey phase without explicit confirmation.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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