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    repobrain

    1

    Grounded repository analysis for code discovery, flow tracing, impact assessment, and merge-readiness reviews.

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    repobrain

    repobrain

    Example session with this skill installed

    Analyze the impact of changing the 'AuthToken' schema in the auth module and check if we are ready to ship these changes.

    • Read your context and instructions
    • Compiled the repobrain

    I have analyzed the impact of the AuthToken schema change and assessed the current repository state for merge readiness. The blast radius is concentrated in the session management and middleware layers.

    Repository Analysis: AuthToken Schema Change

    MetricResultNotes
    Primary Impactsrc/auth/schemas.pySchema definition modified at line 42.
    Downstream Effectssrc/middleware/session.py3 call sites affected in validate_token.
    Dependency RiskHighsrc/api/v1/endpoints.py relies on the legacy field name.
    Confidence0.88 (High)Static call chain is well-defined for this symbol.
    Ship ReadinessBLOCKERMissing test coverage for the new schema validation.

    Impact Summary

    Changing the AuthToken schema affects the session_store lookup logic. While the auth module is updated, the api/v1 routers still attempt to access the deleted expires_at_timestamp field, which will cause a KeyError at runtime.

    Patch Risk

    The patch in the working tree introduces a breaking change to the internal API. RepoBrain flagged 2 warnings regarding unhandled exceptions in the middleware layer during the trace_flow analysis of the new token validation sequence.

    Next steps

    • Update the field references in src/api/v1/endpoints.py to match the new schema.
    • Run search_codebase for "token validation tests" to identify missing test cases.
    • Re-run assess_ship_readiness after adding validation tests for the new schema.
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    • Delivered live via MCP (optional)

    About this skill

    Codebase analysis often relies on guesswork or incomplete Grep results. This skill grounds your agent in a local index to provide evidence-based repository exploration and risk assessment. It moves beyond simple text matching to understand symbol discovery, execution flows, and dependency graphs.

    What it does

    • Trace execution flows by mapping call chains and dependency edges across the repository.
    • Analyze change impact to estimate the blast radius of a code modification before you commit.
    • Review patches for risk by analyzing the working tree or specific Git base-refs.
    • Suggest edit targets to identify which files and symbols require changes for a specific feature.
    • Assess ship readiness by reporting blockers, missing evidence, and production risks.

    How it works

    1. Index the repository to create a local grounded memory of the codebase.
    2. Search the codebase using natural language queries to locate specific logic or symbols.
    3. Trace and analyze components to visualize how data flows and how dependencies interact.
    4. Evaluate changes through patch reviews and ship-readiness assessments before merging.

    Frameworks & tools

    Built for Python 3.12+ environments. Requires Git for patch review and working-tree analysis. Compatible with integrations supporting stdio-json transport for local-first repository indexing.

    Why this beats prompting it yourself

    Standard prompts lack the context of a full repository index and often hallucinate file paths or call chains. This skill uses grounded tools to verify snippets, line spans, and dependency edges, ensuring every recommendation is backed by actual source code evidence.

    Use cases

    • Debugging complex runtime flows across multiple microservices or modules.
    • Onboarding to a large legacy codebase by mapping symbol interactions.
    • Performing pre-merge safety checks to identify high-risk architectural changes.
    • Planning refactors by identifying all affected call sites and dependencies.

    Known limitations

    Requires a local index in a .repobrain/ directory. Static analysis hints are not proof of every runtime path. Remote providers may transmit code snippets depending on user configuration.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Requires

    Python
    Serve MCP

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