What is the 'search-first' approach in this skill?

The search-first approach is a methodology that requires the AI to explore the codebase and gather evidence of the current state before attempting to implement any changes or solutions.

What happens if a required tool hook is missing?

The skill includes fallback logic to use documented scripts within the environment or mark non-applicable checks as SKIP, preventing hard failures during execution.

How does this skill work with OpenClaw?

It translates high-level engineering intents into specific, tool-backed workflows compatible with OpenClaw, ensuring that execution is deterministic and reproducible.

Why does the skill emphasize incremental execution?

Executing in small, safe increments allows the AI to verify the outcome of each step, reducing the surface area for errors and ensuring that every action is backed by verifiable evidence.

ECC Search-First Exploration

Name: ECC Search-First Exploration
Author: oabdelmaksoud

byoabdelmaksoud

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생산성 및 워크플로

Enforces a research-oriented workflow that prioritizes evidence gathering and incremental execution for reliable agent-based development.

The ecc-search-first skill provides Claude with a structured 'search-first' methodology designed for complex agentic execution. It bridges high-level architectural intent with concrete tool steps by loading upstream summaries and translating them into verifiable actions within the OpenClaw ecosystem. By mandating an evidence-first approach and breaking tasks into the smallest safe increments, this skill ensures that AI-driven implementations are grounded in repo-specific data rather than general assumptions, making it ideal for serious engineering tasks where precision is paramount.

주요 기능

01Incremental execution strategy to minimize risks during complex tasks.

02Research-driven workflow prioritizing evidence gathering before implementation.

030 GitHub stars

04Deterministic verification requirements for all reported outcomes.

05Built-in fallback handling for unavailable hooks or environment-specific tools.

06Automated translation of upstream ECC intent into native OpenClaw tool steps.

사용 사례

01Investigating complex codebases to gather evidence before starting refactors.

02Standardizing AI behavior for high-stakes code exploration and verification tasks.

03Executing multi-step agentic workflows that require alignment with upstream project documentation.

주요 기능

01Incremental execution strategy to minimize risks during complex tasks.

02Research-driven workflow prioritizing evidence gathering before implementation.

030 GitHub stars

04Deterministic verification requirements for all reported outcomes.

05Built-in fallback handling for unavailable hooks or environment-specific tools.

06Automated translation of upstream ECC intent into native OpenClaw tool steps.

사용 사례

01Investigating complex codebases to gather evidence before starting refactors.

02Standardizing AI behavior for high-stakes code exploration and verification tasks.

03Executing multi-step agentic workflows that require alignment with upstream project documentation.