Opencode Coding
by Roy Yuen
Enforce senior-level coding standards with a focus on verification, minimal diffs, and evidence-based bug fixing.
- Execute code locally to verify bug fixes with empirical evidence
- Apply minimal code diffs to prevent accidental regressive changes
- Validate technical assumptions through pre-implementation trial runs
$5
· or 25 creditsSecure checkout via Stripe
Included in download
- Execute code locally to verify bug fixes with empirical evidence
- Apply minimal code diffs to prevent accidental regressive changes
- Ready for Claude Code
- Includes example output and usage patterns
Sample input
Audit the authentication service for timing attack vulnerabilities. If found, implement the most surgical fix possible and verify it against existing tests.
Sample output
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.
Opencode Coding
by Roy Yuen
Enforce senior-level coding standards with a focus on verification, minimal diffs, and evidence-based bug fixing.
$5
· or 25 creditsSecure checkout via Stripe
Included in download
- Execute code locally to verify bug fixes with empirical evidence
- Apply minimal code diffs to prevent accidental regressive changes
- Ready for Claude Code
- Includes example output and usage patterns
- Instant install
Sample input
Audit the authentication service for timing attack vulnerabilities. If found, implement the most surgical fix possible and verify it against existing tests.
Sample output
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.
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.
Use Cases
- Execute code locally to verify bug fixes with empirical evidence
- Apply minimal code diffs to prevent accidental regressive changes
- Validate technical assumptions through pre-implementation trial runs
- Enforce senior-level design patterns via strict workflow constraints
Known Limitations
- Effectiveness depends on the quality of existing project test suites.
- Cannot physically run code in environments without an execution runtime.
- High-level architectural redesigns may be limited by the "minimal diff" focus.
How to Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/opencode-coding -o /tmp/opencode-coding.zip && unzip -o /tmp/opencode-coding.zip -d ~/.claude/skills && rm /tmp/opencode-coding.zipFree skills install directly. Paid skills require purchase - use the download button above after buying.
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Permissions
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Claude Code, GitHub Copilot, Cursor, Codex CLI, and SKILL.md-compatible agents.
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