Opencode Coding

    3

    Enforce senior-level coding standards with a focus on verification, minimal diffs, and evidence-based bug fixing.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    opencode-coding

    Example session with this skill installed

    Audit the authentication service for timing attack vulnerabilities. If found, implement the most surgical fix possible and verify it against existing tests.

    • Read your context and instructions
    • Compiled the opencode-coding

    Verified: Ran pytest tests/auth/ (3 passed).
    Inferred: DB connection pool handles the retry logic based on line 42 config.
    Unknown: Scaling behavior under 10k concurrent reqs.
    Change: Modified auth_service.py L89 to use safe_compare to prevent timing attacks. Smallest safe fix.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Execute code locally to verify bug fixes with empirical evidenceApply minimal code diffs to prevent accidental regressive changesValidate technical assumptions through pre-implementation trial runsEnforce senior-level design patterns via strict workflow constraints

    About this skill

    What it does

    Opencode Coding is a high-performance skill designed to enforce senior-engineer coding standards across any AI model. It moves beyond "prompt-and-hope" coding by mandating a rigorous technical workflow: verify first, implement the narrowest defensible change, and prove success through execution rather than inspection.

    Why use this skill

    Standard LLM coding often suffers from "hallucinated confidence" and bloated, speculative refactors. This skill solves that by forcing the agent to adopt a Codex-grade standard. It is better than simple prompting because it embeds a systematic engineering contract: every change must be localized, every bug must be reproduced, and every completion must state exactly what was verified and what remains unknown. It turns your agent into an engineer that values stability and evidence over cleverness.

    Key Features

    • Evidence-Based Debugging: Identifies root causes and reproduces failures before proposing fixes.
    • Minimal Impact Diffs: Prioritizes the smallest safe change to preserve project patterns and reduce regression risk.
    • Verification-First Workflow: mandates running targeted tests, linters, or manual validations before reporting success.
    • Standardized Reporting: Every output includes a "Response Contract" detailing what was Verified, Inferred, and Unknown.

    Supported Use Cases

    This skill is framework-agnostic and works across any tech stack. Use it for complex feature implementation, surgical bug fixing, safe refactoring of legacy modules, and rigorous PR reviews where functional correctness is the priority.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Recently published to Agensi

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    Trust & safety

    Security scanned

    Verified clean 6 months ago

    • Passed all security checks, Safe to install

    Listed6 months ago

    What's inside

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