459 skills · sorted by relevance
Inorganic chemistry, physical chemistry, and materials science — crystal structures, coordination chemistry, lattice parameters, thermodynamic properties, electronic structure. Use for unit cell volume calculations, coordination geometry, materials property estimation, and inorganic-mechanism reasoning. Complementary to tooluniverse-organic-chemistry.
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Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.
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15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.
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Rigorous benchmarking of multi-agent coordination systems: experiment design, statistical analysis, human evaluation protocols, and reproducible reporting. NOT FOR ML model evaluation (use llm-evaluation-harness), A/B testing for web products, survey design, or general data science.
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Drafting and iterating sections of The Federated Harbor whitepaper — the third paper in the Curiositech sequence after Anchor (local identity) and Bonded Commons (local coordination), extending coordination across machines and administrative domains. Use when authoring a section, rewriting a section after a redteam/whitehat round, or composing a new chapter. NOT for marketing copy, NOT for the other two papers, NOT for production threat response.
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Foundational distributed computing algorithms for consensus, coordination, and fault tolerance
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Apply GPGP/TAEMS coordination as local-scheduler constraints under explicit task, view, commitment, and delivery assumptions. NOT for assuming global task visibility, reliable delivery, or modern execution authority.
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Frame multi-agent designs through explicit environment, autonomy, information, coordination, commitment, incentive, and verification assumptions. NOT for endorsing multi-agent architecture without need or claiming runtime correctness from conceptual methods.
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Design a normalized, machine-readable "work receipt" that answers what changed, why, by whom, and with what validation, for any AI-coding-agent task regardless of backend (Claude Code, Codex, Cursor, Aider, CI, or homegrown). Use when defining a receipt schema, normalizing heterogeneous agent logs into one evidence shape, gating PR merges on artifact-backed proof, or designing an OSS maintainer's fast-reject/fast-merge workflow. NOT for validating a DAG node's output against its own schema (use output-contract-enforcer), continuous runtime monitoring of a live coordination daemon (use runtime-verification-for-agents), or general Port Daddy claims/notes/salvage mechanics (use port-daddy-agent-skill).
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Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.
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Design scheduling agents. Use for timezone-safe negotiation, focus protection, and privacy-safe free-busy handling. NOT for calendar UI, project planning, or general agent architecture.
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Agent-to-Agent (A2A) executor implementation patterns for task handling, execution management, and agent coordination. Use when building A2A executors, implementing task handlers, creating agent execution flows, or when user mentions A2A protocol, task execution, agent executors, task handlers, or agent coordination.
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Mechanism conflict detection and coordination for AI assistant systems. Automatically detects crontab conflicts, systemd service conflicts, script overlaps, and log path inconsistencies. Use for maintaining system harmony and preventing internal conflicts.
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Orchestrates multi-agent AI systems with task delegation, agent communication, shared memory, and workflow coordination. Use when users request "multi-agent system", "agent orchestration", "AI agents", "agent coordination", or "autonomous agents".
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Coordinate multiple AI agents with assigned owners, clear work boundaries, and a Kanban board that tracks progress. Organize separate branches or worktrees, collect test and handoff evidence, and define checks that must pass before combining their work.
Triage GitHub issues and pull requests, classify their next steps, and connect active work to Linear. Keeps public updates in GitHub and internal ownership and priorities in Linear, with a clear recommendation for the next action.
Set up and operate Hermes Agent across terminal, desktop, and messaging interfaces. This guide covers model configuration, themes, extensions, and coordination of independent agent instances, with references for task-specific settings and troubleshooting.
Coordinate agent work through the Paperclip API by checking assignments, updating task status, posting progress, delegating subtasks, and requesting approvals. It records deliverables and dependencies so reviewers and other agents can follow the work and continue it.
Coordinate multiple AI agents with agentic-flow to run tasks in parallel or through ordered stages. Configure shared memory, team structure, workload distribution, retries, and task reassignment, then monitor how the group performs.
Organize software releases on GitHub, from version suggestions and release notes to testing, builds, and deployment. Coordinate multiple packages or repositories, roll changes out in stages, and configure rollback to restore an earlier release when problems occur.
Manage GitHub issues, synchronize project boards, and plan sprints, short development cycles. Coordinate multiple AI agents to break work into tasks, assign responsibilities, update progress, and produce reports on completion and blockers.
Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.
Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.
Review GitHub pull requests, or proposed code changes, with specialized agents covering security, performance, architecture, and style. Generate actionable comments, apply quality thresholds, and coordinate automated review workflows.