Latest Agent Skills

When the user wants to optimize any form that is NOT signup/registration — including lead capture forms, contact forms, demo request forms, application forms, survey forms, or checkout forms. Also use when the user mentions "form optimization," "lead form conversions," "form friction," "form fields," "form completion rate," or "contact form." For signup/registration forms, see signup-flow-cro. For popups containing forms, see popup-cro.

Multi-perspective dialectical reasoning with cross-evaluative synthesis. Spawns parallel evaluative lenses (STRUCTURAL, EVIDENTIAL, SCOPE, ADVERSARIAL, PRAGMATIC) that critique thesis AND critique each other's critiques, producing N-squared evaluation matrix before recursive aggregation. Triggers on /critique, /dialectic, /crosseval, requests for thorough analysis, stress-testing arguments, or finding weaknesses. Implements Hegelian refinement enhanced with interleaved multi-domain evaluation and convergent synthesis.

Analyze test coverage gaps. Use when user says "test coverage", "what's not tested", "coverage gaps", "missing tests", "coverage report", or "what needs testing".

Build AI agents and apps with Composio - access 200+ external tools with Tool Router or direct execution

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.

See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, video), and create real time alerts for events from live streams or desktop capture.

Optimizes SQL queries, designs database schemas, and troubleshoots performance issues. Use when a user asks why their query is slow, needs help writing complex joins or aggregations, mentions database performance issues, or wants to design or migrate a schema. Invoke for complex queries, window functions, CTEs, indexing strategies, query plan analysis, covering index creation, recursive queries, EXPLAIN/ANALYZE interpretation, before/after query benchmarking, or migrating queries between database dialects (PostgreSQL, MySQL, SQL Server, Oracle).

Never show the user a first draft. Run every writing task through a self-critique loop — draft, attack the draft as the harshest reviewer in the room, rewrite, and repeat until a full review pass finds zero flags — then return only the final plus a change log. Use for any writing the user actually cares about: emails, LinkedIn posts, newsletters, docs, announcements, client messages. Trigger whenever the user says 'run the loop', 'self-critique this', 'make it bulletproof', 'don't give me a first draft', 'be brutal', or hands over a task where quality matters more than speed. This is a single-agent loop; for the three-agent version use the-team.

Graduate a proven pattern from auto-memory (MEMORY.md) to CLAUDE.md or .claude/rules/ for permanent enforcement.

Generate data-driven user personas for UX research and product design. Usage: /persona generate [options]

Holarchic reasoning framework implementing λ-calculus over simplicial complexes. Entities (ο) transform through operations (λ) toward terminals (τ) via the universal form λο.τ. Persistent homology captures multi-scale structure; sheaf theory ensures local-to-global consistency. Use when knowledge requires: (1) homoiconic self-reference where structure mirrors content, (2) scale-invariant holonic decomposition, (3) topological invariants preserved across transformations, or (4) formal Lex-style axiom systems over property graphs.

Migrate from Cypress or Selenium to Playwright. Use when user mentions "cypress", "selenium", "migrate tests", "convert tests", "switch to playwright", "move from cypress", or "replace selenium".

Compress the entire current conversation into a clean, structured handoff document that lets a new chat session, a colleague, or future-you resume the work without losing decisions, constraints, or progress. Use this skill whenever the user says "handoff", asks to summarize the thread to continue elsewhere, mentions hitting context limits or the chat getting too long, wants to brief a teammate on this conversation, or asks to export or save where things stand. ALSO offer it proactively when a long working thread is clearly winding down or the user says they'll "pick this up later". Do NOT use for summarizing external documents or meetings; this skill summarizes the conversation itself.

Turn a proven pattern or debugging solution into a standalone reusable skill with SKILL.md, reference docs, and examples.

Connect Claude to any app. Send emails, create issues, post messages, update databases - take real actions across Gmail, Slack, GitHub, Notion, and 1000+ services.

Memory health dashboard showing line counts, topic files, capacity, stale entries, and recommendations.

Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.

Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.

Generate knowledge schemas and ontologies from any input format. Extract semantic structures, relationships, and hierarchies. Output as Obsidian markdown with YAML frontmatter, wikilinks, tags, and mermaid diagrams, or export to semantic formats (JSON-LD, RDF, Neo4j Cypher, GraphQL). Supports fractal mode (strict hierarchical constraints) and free mode (flexible generation). Auto-activates for queries containing "schema", "ontology", "knowledge graph", "extract structure", or "generate outline".

Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics.

Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.

Generate test report. Use when user says "test report", "results summary", "test status", "show results", "test dashboard", or "how did tests go".

Understand through recursive decomposition and modular reconstruction of simple operations to reproduce emergent complexity. Use this skill whenever reasoning is required. Framework for [[#parse|parsing]], [[#branch|branching]], [[#reduce|reducing]], [[#ground|grounding]] and [[#emit|emitting]]. Employs metacognitive reasoning epistemology, leveraging first principles through recursive decomposition[^1]. Self-referential and scale-invariant.

Semantic NLP-based code search using neural embeddings and hybrid ranking

Universal transformation λ(ο,K).τ with recursive self-improvement. USE WHEN routing reasoning, validating knowledge graphs, preparing CICM/ANZCA examinations, or when self-improvement of reasoning/architecture/context is required. Routes queries through R0-R3 complexity pipelines, validates topology (η≥target) and governance (KROG), emits per style (Φ), and compounds learnings into knowledge K. Triggers on complexity assessment, multi-step reasoning, examination mode, or /λ invocation.

Use when Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide techniques to avoid over-engineering and apply iterative improvements.

Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.

Optimize content to get cited by AI search engines — ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini, Copilot. Use when you want your content to appear in AI-generated answers, not just ranked in blue links. Triggers: 'optimize for AI search', 'get cited by ChatGPT', 'AI Overviews', 'Perplexity citations', 'AI SEO', 'generative search', 'LLM visibility', 'GEO' (generative engine optimization). NOT for traditional SEO ranking (use seo-audit). NOT for content creation (use content-production).

Cognitive enhancement toolkit combining non-linear reasoning, mental models, and literate programming. Use for complex analysis, system design, debugging, deep learning, and research workflows.

Teleological physiology analysis framework for understanding biological systems through multi-constraint optimization. Use when analyzing physiological mechanisms, explaining apparent biological "inefficiencies", preparing for medical examinations (CICM/ANZCA Primary), understanding why biological systems are designed the way they are, or when seeking deeper mechanistic understanding beyond descriptive knowledge. Triggers on questions like "why is X designed this way", "what purpose does Y serve", "how is Z optimized", analysis of physiological trade-offs, or exploration of evolutionary/design constraints.

Launch N parallel subagents in isolated git worktrees to compete on the session task.

Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator.

Personal working rules, decision frameworks, and preferences for ejarbe. Invoke with /rules to surface these during any session. AUTO-TRIGGER when working with MCP servers, MCP transport, SSE, streamable HTTP, server migration, .well-known metadata, or MCP server discovery/registration.

Merge the winning agent's branch into base, archive losers, and clean up worktrees.

Recursive self-improving holon λ(ο,Κ,Σ).τ' for knowledge compounding and schema evolution. USE WHEN learning, improving, optimizing, assessing, reflecting, debugging, synthesizing, or refining—whether human, AI, or organizational. Triggers on /learn, /compound, /improve, /refine, /optimize, /assess, /reflect, "lessons learned", "best practices", "continuous improvement". Preserves Κ-monotonicity, η≥4, homoiconicity.

Local RAG indexing with 97% storage reduction via anchor-based lazy recomputation. Graph-based selective embedding storage for memory-efficient semantic code search.

Use when extracting entities and relationships, building ontologies, compressing large graphs, or analyzing knowledge structures - provides structural equivalence-based compression achieving 57-95% size reduction, k-bisimulation summarization, categorical quotient constructions, and metagraph hierarchical modeling with scale-invariant properties. Supports recursive refinement through graph topology metrics including |R|/|E| ratios and automorphism analysis.

Optimize prompts via process goals (controllable behavioral instructions) rather than outcome goals (sparse end-result demands). Grounded in sports psychology meta-analysis showing process goals (d=1.36) vastly outperform outcome goals (d=0.09). Use when designing prompts, optimizing LLM steering, implementing CoT/decomposition patterns, or building automatic prompt optimization pipelines. Instantiates surrogate loss paradigm for discrete prompt space.

Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.

Execute OpenAI Codex CLI for code analysis, refactoring, and automated editing. Also use for delegating complex debugging and research to GPT models for second opinions.

Read, write, and browse the AgentHub message board for agent coordination.

Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

Generates, remixes, polls, lists, downloads, and deletes Sora videos via OpenAI's video API using a bundled CLI.

Presentation creation, editing, and analysis. When Claude needs to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or editing content, (3) Working with layouts, (4) Adding comments or speaker notes, or any other presentation tasks

Maximally Endowed Graph Architecture — λ-calculus over bounded n-SuperHyperGraphs with grounded uncertainty, conditional self-duality, and autopoietic refinement. Use when (1) simple graphs insufficient (η<2), (2) multi-scale reasoning required, (3) uncertainty is structured not stochastic, (4) knowledge must self-refactor. Pareto-governed: complexity added only when simpler structures fail validation.

Start an autonomous experiment loop with user-selected interval (10min, 1h, daily, weekly, monthly). Uses CronCreate for scheduling.

Set up Playwright in a project. Use when user says "set up playwright", "add e2e tests", "configure playwright", "testing setup", "init playwright", or "add test infrastructure".

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming. Use when you need to build complex AI systems, program LMs declaratively, optimize prompts automatically, create modular AI pipelines, or build RAG systems and agents.

Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of "Word doc", "word document", ".docx", or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a "report", "memo", "letter", "template", or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

Executable documentation governance with compound engineering and abductive learning. Enforces the Seven Laws through type compilation, schema validation, and hookify-based enforcement. Implements programmatic compound engineering where K' = K ∪ crystallize(assess(τ)) for monotonic knowledge growth. Integrates abstracted abductive learning (OHPT protocol) for systematic debugging and pattern extraction. Trigger when writing code, debugging, establishing governance, or when mentioned vibecode, compound, abductive, or executable documentation. Self-validating and homoiconic.

Universal Reasoning Framework implementing λο.τ calculus over holarchic structures. Provides severity-based routing (R0-R3 pipelines), modular cognitive architecture (DEC, EVL, PAT, SYN, MEA, HYP, INT), fractal execution patterns, multi-level validation (η≥4, KROG), and adaptive learning. Triggers on: (1) complex multi-step reasoning, (2) high-stakes decisions requiring validation, (3) research synthesis across domains, (4) system design and architecture, (5) crisis management, (6) performance optimization. Implements scale-invariant reasoning from micro (tool calls) through meso (skill composition) to macro (orchestrated workflows).

Generate tests, analyze coverage, and run TDD workflows. Usage: /tdd <generate|coverage|validate> [options]

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

Use when tasks involve reading, creating, or reviewing PDF files where rendering and layout matter; prefer visual checks by rendering pages (Poppler) and use Python tools such as `reportlab`, `pdfplumber`, and `pypdf` for generation and extraction.

Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, P

Fix failing or flaky Playwright tests. Use when user says "fix test", "flaky test", "test failing", "debug test", "test broken", "test passes sometimes", or "intermittent failure".

Use when the task involves reading, creating, or editing `.docx` documents, especially when formatting or layout fidelity matters; prefer `python-docx` plus the bundled `scripts/render_docx.py` for visual checks.

Full-stack task execution pipeline — discovers tools via semantic search, routes to the right agent, then executes via GODMODE. Chains manus-tool-discovery → manus-orchestrator → godmode-system in sequence.