テスト・セキュリティのスキル

Analyze git branches and generate structured MR/PR descriptions with domain-based change categorization — bug fixes, enhancements, technical debt, documentation, testing, build/CI, and non-functional changes. Use when preparing merge request descriptions, pull request bodies, writing changelogs from git diffs, documenting branch changes, or generating release notes. Works with GitHub and GitLab without requiring JIRA or issue tracker integration.

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Produces a structured, end-to-end linear walkthrough of an unfamiliar codebase by orchestrating parallel subagents across four phases — discovery, tracing, validation, and synthesis. Use when onboarding to a new repository, understanding execution paths from entry points, generating navigable codebase documentation, or needing fact-checked coverage of architecture, deployment, testing, and operations.

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Create one-off outbound phone calls, web calls, scheduled calls, and simple batch calls using the Vapi API. Use when making or testing individual calls or initiating a bounded /call request programmatically. Use create-campaign for a persistent multi-contact Campaign with lifecycle, reporting, cancellation, duplication, or campaign webhooks.

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A diagnostic tool for OpenClaw agents -- checks skill registry connectivity, AGENTS.md setup, config file validity, and installed skill health. Use when your setup seems broken, skills aren't loading, or you want to audit your agent's configuration.

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Score recorded comparisons again by a tree's weighted measures, without models or spend. Read which promotions the score supports, which measure won each task, the A/A controls, and hours and dollars per promotion. Sweep the weights and the level by flag, and compare a candidate tree that changes a floor or the test.

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The harnesses a campaign keeps, read outside a run: which there are and where each came from, what each is made of and what moved from its parent, what each was measured to be, trying one on a task, the audit before a promotion and the blind review at a handover, whether a part earns its place, and changing what makes a harness or what a seat is told about one. Use when the operator asks which harness is current, what a Builder changed, whether a harness is ready to promote or hand over, to run a kept harness on a task, or to change the harness contract. Reading one run is whole-run-investigation's, wording a claim attribution-and-proof's.

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Rewrite one compartment of Anabasis v2 test-first, with the old code as an oracle. Use when the operator asks to rewrite, overhaul or rebuild code, or says commit R; also within an authorised change when the old shape needs another exception or about half the machinery could give the same result. A stack overhaul belongs to stack-pr.

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Test an uncertain Anabasis v2 path at the smallest real layer that can decide it: one fact from a log or a dry run, a walk of one value across the layers, a replay through this tree's readers, a round or campaign rehearsed on a seed with scripted seats and no Referee, or one live seat from a seed. Use to test a join, revisit a recorded position, size a comparison, or check what a kill or stop leaves. Not for reading a finished run (whole-run-investigation) or starting a paid one (run-management).

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Run the Anabasis v2 improvement loop, the Super Loop, and the release cycle around it: fork fresh campaigns at the last release, read each at its milestones and resolve predictions, then stop them, investigate them, plan and implement the patches, land the release, and only then fork the next cycle. Inside the cycle each pass scores the five points, finds the link that holds the next promotion, fixes its owner, proves the path and predicts. Use when the operator asks to keep campaigns going, fork runs, work towards a release, run a pass or an hourly audit, read a promotion, investigate a campaign that is not promoting, or choose the next run. Owns the experiment and its evidence; AGENTS.md section 3 owns the operator's decisions.

日本語の概要は準備中です。原文の説明を表示しています。

Create and maintain AgentV YAML evaluation files for testing AI agent performance. Use this skill when creating new eval files, adding eval cases, or configuring custom evaluators (code validators or LLM judges) for agent testing workflows.

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Audit codebase for agentic layer coverage and identify gaps. Use when assessing agentic layer maturity, identifying investment opportunities, or evaluating primitive coverage.

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Testing guidelines for Jarvy CLI - unit testing patterns, integration tests with assert_cmd, test environment variables, platform-specific testing, and CI coverage strategies.

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Dependency management guidelines for Jarvy - crate selection criteria, feature flag best practices, version management, security auditing with cargo-audit and cargo-deny.

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Security best practices and guidelines for the Jarvy CLI codebase - a cross-platform development environment provisioning tool that executes system commands with elevated privileges

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Create or update root and nested AGENTS.md files that document scoped conventions, monorepo module maps, cross-domain workflows, and (optionally) per-module feature maps (feature -> paths, entrypoints, tests, docs). Use when the user asks for AGENTS.md, nested agent instructions, or a module/feature map.

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Operate E2B agent sandboxes using the CLI. Use when user needs to run code in isolation, test packages, execute commands safely, or work with binary files in a sandbox environment. Keywords: sandbox, e2b, isolated environment, run code, test code, safe execution.

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Optimize ElevenLabs conversational AI agents for real estate applications. Use when creating new agents, improving conversation quality, selecting voices, engineering system prompts, configuring agent parameters, or analyzing agent performance metrics. Includes voice selection, model tuning, prompt engineering, and A/B testing strategies.

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Create and manage AI agent sessions with multiple backends (SDK, Claude CLI, Codex, Cursor). Also supports multi-agent workflows with shared context, @mention coordination, and collaborative voting. Use for "start agent session", "create worker", "run agent", "multi-agent workflow", "agent collaboration", "test with tools", or when orchestrating AI conversations programmatically.

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LLM prompt management and evaluation platform. Version prompts, run A/B tests, evaluate with metrics, and deploy with confidence using Agenta's self-hosted solution.

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Test agent delegation patterns to verify hierarchy and escalation paths. Use after modifying agent structure.

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Create .agent/baseline.md and later compare against it. Use when capturing baseline build/lint/test results or investigating newly introduced findings.

日本語の概要は準備中です。原文の説明を表示しています。

Evaluate a codebase for agent-friendliness based on autonomous agent best practices. Use when asked to "evaluate for agents", "check agent readiness", "audit for autonomous execution", "assess agent-friendliness", or when reviewing infrastructure for unattended agent operation. Also use when asked about making a codebase more suitable for AI agents or autonomous workflows.

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Handle failures and errors during workflow. Use when build breaks, tests fail unexpectedly, or agent gets stuck. Semi-automatic recovery with user confirmation for destructive actions.

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Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

日本語の概要は準備中です。原文の説明を表示しています。