Topic: claude
14,433 skills in this topic.
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test-skill
Use when testing per-client skill rules
agent-sh/agnix 169
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invalid-context-skill
Use when testing invalid context validation
agent-sh/agnix 169
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with-argument-hint
Use when greeting users
agent-sh/agnix 169
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hooks-skill
Use when testing hooks validation
agent-sh/agnix 169
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bad-invocable-skill
Use when testing boolean type validation
agent-sh/agnix 169
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test-skill
Use when testing per-client skill rules
agent-sh/agnix 169
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invalid-agent-skill
Use when testing invalid agent validation
agent-sh/agnix 169
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test-skill
Use when testing per-client skill rules
agent-sh/agnix 169
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hint-skill
Use when testing argument hints
agent-sh/agnix 169
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missing-frontmatter
agent-sh/agnix 169
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helper
Use when validating vague skill names
agent-sh/agnix 169
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unknown-frontmatter-field
Use when validating frontmatter keys
agent-sh/agnix 169
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test-skill
Use when testing per-client skill rules
agent-sh/agnix 169
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agnix
Use when user asks to 'lint agent configs', 'validate skills', 'check CLAUDE.md', 'validate hooks', 'lint MCP'. Validates agent configuration files against 385 rules across 10+ AI tools.
agent-sh/agnix 169
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deep-reference
Use when validating deep references
agent-sh/agnix 169
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directory-helper
Use when validating name-directory matching
agent-sh/agnix 169
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reference-skill
Use when looking up documentation
agent-sh/agnix 169
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unknown-tool-skill
Use when testing unknown tool validation
agent-sh/agnix 169
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fork-with-instructions
Use when analyzing code quality
agent-sh/agnix 169
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invalid_name
Use when user wants to review code
agent-sh/agnix 169
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first-person-description
Use when you need pull-request review help
agent-sh/agnix 169
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agent-without-context-skill
Use when testing agent without context validation
agent-sh/agnix 169
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grid-ctf-ops
Operational knowledge for the grid_ctf scenario including strategy playbook, lessons learned, and resource references. Use when generating, evaluating, coaching, or debugging grid_ctf strategies.
greyhaven-ai/autocontext 729
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autocontext
Iterative strategy generation and evaluation system. Use when the user wants to evaluate agent output quality, run improvement loops, queue tasks for background evaluation, check run status, or discover available scenarios. Provides LLM-based judging with rubric-driven scoring.
greyhaven-ai/autocontext 729