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Agent Skills

Agent-Skills.md is a agent skills marketplace, to find the right agent skills for you.

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fix-failing-pipelines

This skill should be used when the user asks to "fix pipelines", "fix CI", "check staging pipelines", "fix failing workflows", "fix failing actions", or wants to find and fix failing GitHub Actions workflows on the staging branch of the babysitter repo.

a5c-ai
a5c-ai
1,52087

retrospect-external-babysitter-run

For a repository in the babysitter-users catalog, locate its babysitter processes and any committed runs (.a5c/runs/<runId>/) and perform a retrospective on a chosen run -- what went well, what failed, process suggestions, quality of effect design, breakpoint patterns -- mirroring the /babysitter:retrospect workflow but applied to an external repo. Invoke when asked to "retrospect on repo X's run", "analyze how someone else used babysitter", or "review an external babysitter run".

a5c-ai
a5c-ai
1,52087

architecture-patterns

System and API design guidance covering component boundaries, data flow, integration patterns, and scalability considerations.

a5c-ai
a5c-ai
1,52087

code-generation

Minimal, pattern-matching code output. Write the least code that satisfies requirements. Match existing project patterns. Use Write/Edit tools only.

a5c-ai
a5c-ai
1,52087

debugging-patterns

Root cause analysis frameworks including log-first investigation, git bisect correlation, and pattern-based diagnosis with confidence scoring.

a5c-ai
a5c-ai
1,52087

planning-patterns

Structured planning methodology with research, brainstorming, phased plan creation, risk assessment, and plan-to-build continuity.

a5c-ai
a5c-ai
1,52087

session-memory

Mandatory memory persistence system across session resets using three markdown surfaces in .claude/cc10x/. Iron law - every workflow must load at start and update at end.

a5c-ai
a5c-ai
1,52087

test-driven-development

Strict RED-GREEN-REFACTOR cycle enforcement. Tests are never skipped or deferred. Run mode only, never watch mode. Exit code evidence mandatory.

a5c-ai
a5c-ai
1,52087

verification-before-completion

Evidence requirement enforcement ensuring all claims are backed by logs, test results, or exit codes. Zero = success, non-zero = failure. No guessing allowed.

a5c-ai
a5c-ai
1,52087

checkpoint-management

Git-backed state management for safe rollback. Create and restore checkpoints with tagged commits and metadata tracking.

a5c-ai
a5c-ai
1,52087

mcp-host-styling-integration

Integrates MCP App UI with host theming system. Applies host CSS variables, handles onhostcontextchanged, safe area insets, display mode detection, and fullscreen configuration.

a5c-ai
a5c-ai
1,52087

executing-plans

Use when you have a written implementation plan to execute in a separate session with review checkpoints between batches.

a5c-ai
a5c-ai
1,52087

cross-artifact-analysis

Perform cross-artifact consistency and coverage analysis across constitution, specification, plan, and task artifacts to detect gaps, conflicts, and misalignments before implementation.

a5c-ai
a5c-ai
1,52087

self-optimization

SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.

a5c-ai
a5c-ai
1,52087

security-hardening

AIDefence security layer with prompt injection blocking, input validation, sandboxed execution, output sanitization, and STRIDE threat modeling.

a5c-ai
a5c-ai
1,52087

consensus-mechanisms

Multi-protocol consensus for agent swarms supporting Raft leader election, Byzantine fault tolerance, Gossip state propagation, and CRDT conflict-free merging.

a5c-ai
a5c-ai
1,52087

anti-drift

Hierarchical coordination and drift detection with frequent checkpoints, shared memory coherence validation, role specialization enforcement, and short task cycles.

a5c-ai
a5c-ai
1,52087

mem0-integration

Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.

a5c-ai
a5c-ai
1,52087

memory-summarization

Conversation summarization for memory compression and context management

a5c-ai
a5c-ai
1,52087

session-management

Manage agent sessions including initialization, handoffs, revival (seance), and persistent identity for Polecats and Crew agents.

a5c-ai
a5c-ai
1,52087

langchain-react-agent

LangChain ReAct agent implementation with tool binding for reasoning and action loops

a5c-ai
a5c-ai
1,52087

langgraph-hitl

Human-in-the-loop integration for LangGraph workflows with approval and intervention points

a5c-ai
a5c-ai
1,52087

mcp-app-verification

Comprehensive verification checklists for MCP Apps. Tests with basic-host reference, validates handler-before-connect, text fallback, resource URI linking, single-file bundling, host styling, CSP, and legacy pattern detection.

a5c-ai
a5c-ai
1,52087

hotfix-triage

Urgent issue classification, root cause analysis, and fast-path routing for production hotfixes

a5c-ai
a5c-ai
1,52087

knowledge-graph-management

Capture, validate, query, and sync architectural patterns and design decisions in the knowledge graph

a5c-ai
a5c-ai
1,52087

maintenance-orchestration

Technical debt management including branch cleanup, doc verification, TODO scanning, and dependency auditing

a5c-ai
a5c-ai
1,52087

requirements-interview

Interactive PM interview with expertise-adaptive questioning for requirements elicitation

a5c-ai
a5c-ai
1,52087

eval-harness

Evaluation harness for testing agent and skill quality through structured benchmarks, regression tests, and quality scoring.

a5c-ai
a5c-ai
1,52087

specification-generation

Convert requirements into structured technical specifications with architecture decisions

a5c-ai
a5c-ai
1,52087

story-decomposition

Break technical specifications into small, implementable stories with dependency ordering

a5c-ai
a5c-ai
1,52087

writing-skills

Use when creating new skills, editing existing skills, or verifying skills work before deployment.

a5c-ai
a5c-ai
1,52087

autonomous-coding-engagement

Autonomously engage with complex coding tasks — multi-step planning, tool use, self-correction, and solution verification without human intervention.

a5c-ai
a5c-ai
1,52087

closed-book-frontier-reasoning

Perform closed-book frontier reasoning — complex problem solving from internalized knowledge without external retrieval or tool use.

a5c-ai
a5c-ai
1,52087

using-superpowers

Use when starting any conversation. Establishes how to find and use skills, requiring skill invocation before any response.

a5c-ai
a5c-ai
1,52087

milvus-integration

Milvus distributed vector database configuration for large-scale RAG applications

a5c-ai
a5c-ai
1,52087

multi-app-orchestration

Orchestrate workflows across multiple applications and APIs — inter-app coordination, data handoff, and multi-system task completion.

a5c-ai
a5c-ai
1,52087

multi-turn-tool-use

Design agents for multi-turn tool use — sequential tool calls, result accumulation, error recovery, and complex task decomposition over multiple turns.

a5c-ai
a5c-ai
1,52087

nemo-guardrails

NVIDIA NeMo Guardrails configuration for conversational safety and control

a5c-ai
a5c-ai
1,52087

requesting-code-review

Use when completing tasks, implementing major features, or before merging to verify work meets requirements.

a5c-ai
a5c-ai
1,52087

writing-plans

Use when you have a spec or requirements for a multi-step task, before touching code. Creates bite-sized TDD implementation plans with dependency tracking.

a5c-ai
a5c-ai
1,52087

cog-weekly-reflection

Cross-domain pattern analysis with personal, professional, and project domain synthesis

a5c-ai
a5c-ai
1,52087

cog-url-extraction

Save URLs with auto-extracted insights, credibility scoring, and vault routing

a5c-ai
a5c-ai
1,52087

setfit-few-shot

SetFit few-shot learning for efficient intent classification with minimal data

a5c-ai
a5c-ai
1,52087

vector-memory

HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.

a5c-ai
a5c-ai
1,52087

cog-team-intelligence

Cross-reference GitHub, Linear, Slack, and PostHog with bidirectional sync for team briefs

a5c-ai
a5c-ai
1,52087

systematic-debugging

Structured debugging methodology using hypothesis-driven investigation, log analysis, and bisection to isolate and resolve defects.

a5c-ai
a5c-ai
1,52087

cog-daily-intelligence

Generate personalized verified news briefs with 7-day freshness and 95%+ source accuracy

a5c-ai
a5c-ai
1,52087

cog-knowledge-consolidation

Build structured knowledge frameworks from scattered vault notes with source attribution

a5c-ai
a5c-ai
1,52087

verification

Verification-before-completion discipline ensuring all success criteria are met, tests pass, and reviews complete before declaring work done.

a5c-ai
a5c-ai
1,52087

cog-meeting-processing

Process meeting recordings and transcripts into decisions, action items, and team dynamics

a5c-ai
a5c-ai
1,52087

Page 316 of 1729 · 86403 results

Adoption

Agent Skills are supported by leading AI development tools.

FAQ

Frequently asked questions about Agent Skills.

01

What are Agent Skills?

Agent Skills are reusable, production-ready capability packs for AI agents. Each skill lives in its own folder and is described by a SKILL.md file with metadata and instructions.

02

What does this agent-skills.md site do?

Agent Skills is a curated directory that indexes skill repositories and lets you browse, preview, and download skills in a consistent format.

03

Where are skills stored in a repo?

By default, the site scans the skills/ folder. You can also submit a URL that points directly to a specific skills folder.

04

What is required inside SKILL.md?

SKILL.md must include YAML frontmatter with at least name and description. The body contains the actual guidance and steps for the agent.

05

How can I submit a repo?

Click Submit in the header and paste a GitHub URL that points to a skills folder. We’ll parse it and add any valid skills to the directory.