Other skills

Use after an Anabasis v2 comparison, checkpoint or system change, and before any sentence claims that a harness, a model or a patch improved something. States what one reading may be said to show: the identities it was made under, the counts kept apart, whether it was repeated, which part of a harness it belongs to, whether a mechanism was live in the run, and whether the measurement is valid as distinct from whether it was exploited. Ends in one fixed report.

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Zip recorded Anabasis v2 runs for handover at a chosen depth (--light, --medium, --verbose). Use when the operator asks to zip, bundle, archive or send a run, a round, its log, its harness or "everything the model saw", or names a size cap. One command, three cumulative levels, any number of runs, a README, a reading guide and a MANIFEST inside every zip.

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Measure whether a change made the tree simpler: line counts, branching, coupling, interface width, rule count, standing exemptions and model-visible prose across one diff or a stack of pull requests. Use before claiming a pass reduced complexity, when deciding which pull request added machinery, and to read a function's branch count, which no v2 gate reports.

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Psychrometric Analysis Agent

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AI-powered issue operations via gh-models. TRIGGERS - issue summarization, auto-labeling, issue insights.

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Advanced multi-agent reasoning system for complex questions requiring maximum accuracy. Uses adaptive technique selection, adversarial validation, multi-agent debate, and deep analysis. Trigger phrases "ai board", "expert panel", "deep analysis", "maximum accuracy", "thorough reasoning". Use for critical medical decisions, theological questions, philosophical analysis, complex technical problems, high-stakes decisions, or any question requiring 95%+ confidence.

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Dynamic agent composition and management system. USE WHEN user says create custom agents, spin up custom agents, specialized agents, OR asks for agent personalities, available traits, agent voices. Handles custom agent creation, personality assignment, voice mapping, and parallel agent orchestration.

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AI agents as force multipliers for quality work. Core skill for all 19 QE agents using PACT principles.

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Inter-agent communication for tmux sessions. Send and receive messages between AI agents.

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Spawn Agentica multi-agent patterns

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How to spawn, manage, message, and command subagents via gobby-agents.

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Agent Lightning를 사용하여 AI 에이전트를 자동으로 최적화하는 방법을 제공합니다.

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Generate a project-specific AGENTS.md from a user goal, then confirm before overwriting.

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Define tools for the support agent. Use when adding new capabilities like refund processing, license transfer, knowledge lookup, or any agent action.

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AgentDB Vector Search Optimization operates on 3 fundamental principles:

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AgentDB Persistent Memory Patterns operates on 3 fundamental principles:

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Validate agent YAML frontmatter and configuration. Use before committing agent changes or in CI.

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Imported skill agent from langchain

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Create tailored resume from job + source resume. Orchestrates the rewriting of bullet points to emphasize relevant experience without fabrication.

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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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Create, use, and manage Agent Skills for Claude. Use when working with Skills, creating custom capabilities, or understanding how Skills extend Claude's functionality. Covers Skill architecture, file structure, and best practices.

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Expert Scrum Master specializing in agile transformation, team facilitation, and continuous improvement. Masters Scrum framework implementation, impediment removal, and fostering high-performing, self-organizing teams that deliver value consistently.

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Skillの割当表（agent -> skills）を作るときのルール集。skill-assigner が mapping を更新するときに参照する。

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AI agent self-correction mechanisms: error detection, validation loops, recovery strategies, confidence scoring, and iterative refinement

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