Agent Workflow Claude Skills
4 122 agent workflow skills from 665 authors. They configure the agents themselves: memory, prompts, context and other skills. Half of them fit into 1 844 tokens or less — that is what one costs your context window when the agent loads it. 778 ship runnable scripts rather than instructions alone. 5 of them cannot work without an MCP server, most often task. We also found 589 copies of these same skills sitting in other people's repositories — counted once here, not 589 times.
4 122 unique 665 authors 4 updated this month 506 from vendors
Agent skill for multi-repo-swarm - invoke with $agent-multi-repo-swarm
This skill should be used when building AI agents using prompt-native architecture where features are defined in prompts, not code. Use it when creating autonomous agents, designing MCP servers, implementing self-modifying systems, or adopting the "trust the agent's intelligence" philosophy.
Make an out-of-session agent AgentOps-native via skills + ao CLI + CI, not hooks.
Comprehensive framework for effective gptme agent onboarding that builds user trust, communicates capabilities clearly, and establishes productive working relationships from the first interaction.
Reviews standalone skills and their configured distribution surfaces before publication. Use for skill release readiness; not for product code or implementation-plan review.
Direct advanced Three.js camera systems. Use for scale-aware chase rigs, thrust lag, side/orbit cameras, body-relative up vectors, quaternion handoffs, authored cinematic framing, floating origins, pointer-look controls, camera collision constraints, projection ownership, and lifecycle restoration.
Route ambitious Three.js graphics work to the smallest expert skill set. Use for new visual experiences, graphics rewrites, reference matching, or requests spanning geometry, materials, atmosphere, shadows, temporal effects, and final image treatment.
Design deliberately flawed examples that develop error-detection skills and deepen understanding. Use when students make characteristic errors and need practice spotting mistakes.
Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles.
Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.
Modify a lesson plan using SIOP sheltered instruction principles to support language learners across all four skills. Use when planning lessons for classes that include EAL students.
Build students' capacity to place historical documents in their temporal and social context. Use when students read sources without considering what was happening at the time, or know the context but don't deploy it.
Build students' capacity to compare accounts across multiple historical sources — identifying agreements, contradictions, and gaps. Use when students treat individual documents as complete answers rather than partial perspectives.
Build students' capacity to read historical documents closely — attending to word choice, tone, and rhetoric as evidence of perspective. Use when students summarise sources without analysing language.
Design formative assessments that make students' historical thinking visible — revealing whether they source, close-read, contextualise, and corroborate. Use when assessing historical thinking skills or planning a diagnostic.
Build students' capacity to interrogate a historical source before reading — asking who authored it, when, why, and what this means for reliability. Use when students read documents without attending to authorship.
Generate elaborative interrogation prompts that deepen encoding through targeted why and how questions. Use when students memorise without understanding or need deeper processing of content.
Design a professional development session using adult learning principles with active teacher engagement. Use when planning INSET days, CPD workshops, or staff training sessions.
Generate structured reflection prompts for a specific teaching experience or professional learning challenge. Use when debriefing lessons, journaling, or preparing for coaching conversations.
Build a library of metacognitive prompts targeting planning, monitoring, or evaluation for a specific task. Use when developing students' thinking-about-thinking during independent work.
Track performance across sessions and reduce scaffolding as competence grows. Makes fading visible — the learner knows when scaffolds are removed and why. Use for sustained learning engagement where independence is the goal.
Before any explanation or answer, require the learner to produce a free-recall attempt and confidence rating. Use when a student wants help understanding or reviewing a topic — this skill ensures the AI works from what the learner already knows.
Wrap a learning session in a plan → monitor → reflect cycle. Use at the start of any substantial study session to set goals, mid-session to check strategy, and at session end to consolidate what changed. Builds self-regulated learning as a habit.
Review the week using accumulated session evidence — retrieval rates, hint depths, calibration accuracy, transfer and unassisted results. The learner identifies patterns and sets a strategy goal. Use weekly or after a multi-session period.
Agent Context Isolation
Agent Orchestration Rules
Steam-style achievement system with XP, levels, streaks, and skill trees. Gamifies the development workflow. 25 achievements across 5 categories.
Framework for measuring and tracking agent response quality over time. Detects regressions before they reach production. Use when evaluating agent changes, auditing quality, or establishing performance baselines.
Agent ve skill dosyalarinin yapisal dogrulamasi. Frontmatter kontrol, naming convention, zorunlu bolum kontrolu, tutarlilik denetimi. Yeni agent/skill eklendiginde veya mevcut dosyalar duzenlediginde otomatik calistirilir.
Write reliable prompts for Agentica/REPL agents that avoid LLM instruction ambiguity
Spawn Agentica multi-agent patterns
Background Agent Pings
Analyze Claude Code sessions via Braintrust
Braintrust tracing for Claude Code - hook architecture, sub-agent correlation, debugging
Workflow orchestrator that chains existing skills for feature development
Transform session learnings into permanent capabilities (skills, rules, agents). Use when asked to "improve setup", "learn from sessions", "compound learnings", or "what patterns should become skills".
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
Create handoff document for transferring work to another session
Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles
Autonomous experiment loop: hypothesize > modify > test > evaluate > keep/discard > repeat. Run N experiments automatically with measurable metrics. Works for performance optimization, A/B testing, prompt engineering, and any measurable improvement task.
Curated catalog of 60+ external Claude Code skills across 8 categories with quality rankings, install instructions, and fallback strategies
Meta-skill workflow orchestrator for bug investigation and resolution. Routes to debug, implement, test, and commit based on scope.
Interactive workspace discovery - learn what tools, workflows, agents, and hooks are available
Hızır'ın kullanım kılavuzu. Tüm komutlar, agent'lar, workflow'lar, sistemler burada. /hizir yaz, her şeyi gör.
Implementation agent that executes a single task and creates handoff on completion
Pattern for progressively refining context retrieval to solve the subagent context problem
MCP server registry, auto-discovery, configuration, custom server development guide