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    Decision Journal

    1

    Capture architectural trade-offs and technical decisions in a searchable, numbered Markdown journal.

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    Decision Journal

    Decision Journal

    Example session with this skill installed

    We decided to use Redis for the caching layer instead of Memcached because we need the data persistence and built-in pub/sub features. Record this as a new decision.

    • Read your context and instructions
    • Compiled the decision journal

    I have recorded the decision to use Redis for the caching layer. A new entry has been created and the index has been updated.

    Decision Recorded: 0005-redis-caching-layer.md

    FieldValue
    ID0005
    Statusaccepted
    TitleUse Redis for caching layer
    DecisionChose Redis over Memcached because we require RDB/AOF persistence and pub/sub capabilities for future real-time updates.
    Tagsinfrastructure, caching, redis
    File Pathdocs/decisions/0005-redis-caching-layer.md

    Entry Preview

    Context
    The application requires a shared cache across multiple instances. While basic key-value storage is the primary need, our roadmap includes real-time notifications and we cannot risk a full cache wipe on service restarts.

    Consequences

    1. Benefit: Reduced latency for pub/sub operations compared to a separate MQ.
    2. Benefit: Durable cache state across deployments.
    3. Cost: Increased operational complexity compared to Memcached's simpler memory model.
    4. Revisit: If memory overhead becomes a bottleneck or persistence is moved to the primary DB.

    Next steps

    1. Review docs/decisions/0005-redis-caching-layer.md to ensure the consequences list is exhaustive.
    2. Commit the new file and the updated docs/decisions/INDEX.md.
    3. Reference this decision ID in the cache initialization module comments.
    0005-redis-caching-layer.md

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    About this skill

    Technical projects often suffer from "lost context" where later developers can see what was built but not why specific trade-offs were made. This skill implements a disciplined Decision Journal (ADR) system within your repository to capture architectural reasoning at the moment of choice. It prevents re-litigating old debates and ensures every technical constraint is documented alongside its rejected alternatives.

    What it does

    • Formalizes trade-offs by enforcing a "Chose X over Y because Z" format for all technical decisions.
    • Automates ADR creation using a provided script to generate numbered, timestamped Markdown entries.
    • Manages decision lifecycles by handling supersession and deprecation to prevent circular reasoning.
    • Indexes architecture in a central INDEX.md for fast grep-based or script-based retrieval.
    • Validates reasoning by ensuring every entry includes context, consequences, and a named alternative.

    How it works

    1. Search the existing journal using decision.sh search to see if a standing decision already covers the current problem.
    2. Initialize a new entry with decision.sh new "<title>" when a choice constrains future work or overrides a default.
    3. Document the specific context, the rejected option, and the long-term consequences in the generated Markdown file.
    4. Supersede old entries when architectural directions change, creating a clear audit trail of the project's evolution.

    Frameworks & tools

    This skill uses Bash scripts for automation and standard Markdown for storage. It is compatible with any repository structure but defaults to docs/decisions/.

    Why this beats prompting it yourself

    Generic LLMs often provide "notes" rather than "decisions," missing the critical rejected alternatives that make an ADR useful. This skill enforces a strict schema that ensures reasoning is actually testable and reversible by future developers.

    Use cases

    • Documenting why a specific database or storage engine was chosen over common alternatives.
    • Recording the reasoning for specific API contract shapes or authentication models.
    • Tracking the evolution of project standards, such as choosing a specific testing library.
    • Capturing "hidden" preferences from senior developers that aren't obvious in the code.

    Known limitations

    Does not support multi-decision entries; each choice requires a unique file to maintain clean supersession chains. Logic is restricted to local filesystem operations.

    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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