Apply test-driven development cycles to create and validate Claude writing-skills.
npx skills add https://github.com/caiokf/homepage --skill writing-skills-caiokf
Please help me install this Agent Skill. Skill: writing-skills Source: https://github.com/caiokf/homepage/tree/main/.claude/skills/writing-skills Command: npx skills add https://github.com/caiokf/homepage --skill writing-skills-caiokf
Creating effective AI skills (documentation for AI agents) is challenging. Untested skills often fail, lead to agent rationalization, and don't reliably guide AI behavior, wasting time and effort. This skill provides a rigorous, test-driven approach to skill development.
Create a new skill for managing project tasks, following the test-driven development approach outlined in the writing-skills guide.
High-intent search queries and answers about installing and using this skill.
Test-driven documentation for AI skills follows the RED-GREEN-REFACTOR cycle: write test cases that verify skill behavior, create the skill documentation to pass those tests, then refactor for clarity. This ensures skills reliably guide AI behavior without loopholes or rationalization.
Use subagent testing: deploy your skill to subagents under pressure scenarios and observe whether they follow instructions or find workarounds. Document failures, close gaps in your skill language, and re-test until the skill performs consistently across edge cases.
Claude Search Optimization requires YAML frontmatter with name and description fields, a standardized SKILL.md layout, and keyword selection that matches how users search for the task. Clear naming and descriptions help AI instances find skills that solve their specific problems.
Untested skills lack explicit constraints that address common AI rationalization patterns. Without pressure testing and documented edge cases, agents discover loopholes and justify deviations from intent. Test-driven skill development closes these gaps systematically.
A valid Claude skill requires YAML frontmatter with name and description fields at the top, followed by a standardized SKILL.md layout. This structure enables proper indexing, discoverability, and consistent formatting across the skill ecosystem.
Yes. Apply the RED-GREEN-REFACTOR cycle to existing skills by identifying failure modes through subagent testing, updating documentation to address those gaps, and re-validating behavior. This iterative approach systematically eliminates rationalization and improves reliability.
pgermishuysAutomates creation and validation of formal feature specifications with YAML frontmatter and quality checklists.
boazcstrikeApply TDD methodology to author and refine AI skills.
xloxn69Validate AI skills using test-driven development and RED/GREEN/REFACTOR cycles.
openakitaEnforce a RED-GREEN-REFACTOR cycle for AI agent skill documentation.
