ChloeVPin/open-agent-skills

contradiction-resolution

Resolve conflicting claims, evidence, tests, or failures without forcing false consensus. Use when sources or observed behavior disagree and the coding agent must preserve uncertainty and decision boundaries.

原文の言語: 英語

dependency-security-audit

Review dependency and supply-chain changes for provenance, vulnerabilities, integrity, execution behavior, and residual risk. Use when manifests, lockfiles, build scripts, or external artifacts change.

原文の言語: 英語

differential-patch-review

Compare a candidate coding change with a trusted baseline or reference to detect observable behavioral divergence. Use when ordinary tests pass but semantic alignment remains uncertain.

原文の言語: 英語

knowledge-maintenance

Keep coding-agent skills and guidance current by tracking provenance, freshness, deprecation, supersession, contradictions, and review triggers. Use when evidence, dependencies, versions, or failures change.

原文の言語: 英語

repository-change-verification

Verify that an AI-generated repository change matches the requested outcome, preserves unrelated behavior, and has adequate evidence before acceptance. Use for patches, commits, and pull requests.

原文の言語: 英語

test-effectiveness-analysis

Evaluate whether tests detect acceptance-relevant behavioral faults using boundaries, negative cases, differential checks, and mutation-oriented diagnostics. Use when tests are added, changed, weak, or contradicted by regressions.

原文の言語: 英語

tool-authorization-audit

Review AI-agent tools and proposed calls for least privilege, bounded parameters, explicit authorization, isolation, and auditable high-impact actions. Use when tools, credentials, scopes, or permissions change.

原文の言語: 英語

accessibility-review

Review user-facing software changes for accessibility barriers across semantics, keyboard and other input, focus, structure, content, feedback, and assistive technology behavior. Use for web, mobile, desktop, CLI, and interactive UI changes; adapt the checks…

原文の言語: 英語

dead-code-and-comment-cleanup

Identify dead code, duplicated literals, drift-prone declarations, and comments that restate implementation without deleting anything until usage and intent are verified. Use when an AI coding agent is asked to clean up unused code or redundant comments.

原文の言語: 英語

api-contract-compatibility

Design, change, or review APIs and serialized interfaces while making compatibility, versioning, errors, and migration behavior explicit. Use for HTTP, RPC, CLI, event, library, configuration, and file-format interfaces used by other code or people.

原文の言語: 英語

configuration-and-secrets-safety

Design and review application configuration, environment handling, and secret use without unsafe defaults, accidental disclosure, or environment drift. Use when an AI coding agent changes config files, environment variables, deployment settings, credentials,…

原文の言語: 英語

repository-exploration

Map an unfamiliar software repository before changing it. Use when an AI coding agent must locate the right files, understand callers and conventions, identify verification commands, or avoid missing hidden interfaces and generated code.

原文の言語: 英語

agent-evaluation-and-benchmarking

Design and interpret evaluations of AI coding-agent behavior across realistic tasks, correctness, safety, usefulness, and maintainability. Use when creating benchmarks, comparing agents or skills, measuring regressions, or making claims about agent capability.

原文の言語: 英語

build-and-ci-integrity

Review and improve build systems and CI workflows for reproducibility, least privilege, trustworthy artifacts, controlled inputs, and repeatable verification. Use when an AI coding agent changes build commands, workflow files, runners, artifact publication,…

原文の言語: 英語

epistemic-coding

Apply an evidence-first philosophy while investigating, changing, or explaining a software repository. Use when correctness, uncertainty, scope, or the evidence behind a coding decision matters.

原文の言語: 英語

evidence-driven-debugging

Diagnose software failures by reproducing the symptom, separating observations from hypotheses, testing competing explanations, and making the smallest verified fix. Use for bugs, regressions, flaky behavior, failing builds, and unexplained runtime errors in…

原文の言語: 英語

internationalization-and-localization

Design and review software for multiple languages, locales, scripts, regions, calendars, currencies, units, and text directions. Use when an AI coding agent adds user-facing text, formatting, sorting, input, translation, or locale-sensitive behavior.

原文の言語: 英語

observability-and-instrumentation

Add or review logs, metrics, traces, and diagnostic signals so AI coding agents can understand real behavior and failures without excessive cost, noise, or data exposure. Use when changing production behavior, debugging intermittent issues, or operating…

原文の言語: 英語

performance-regression-analysis

Investigate and validate software performance changes using representative workloads, baselines, variance-aware measurements, and resource tradeoffs. Use when an AI coding agent is asked to make code faster, reduce latency or cost, increase throughput, or…

原文の言語: 英語

regression-test-design

Design focused regression tests for AI-generated or human software changes from observable behavior, failure modes, and boundaries. Use when fixing a bug, adding behavior, changing an interface, or strengthening a weak test suite.

原文の言語: 英語

release-and-rollback-safety

Plan and review safe software releases with explicit preconditions, staged exposure, health signals, rollback boundaries, and recovery evidence. Use when an AI coding agent changes deployable behavior, migrations, configuration, or release automation.

原文の言語: 英語

resilience-and-retry-design

Design and review timeouts, retries, backoff, circuit breakers, rate limits, bulkheads, and graceful degradation for unreliable dependencies. Use when an AI coding agent changes network calls, queues, workers, caches, or failure handling.

原文の言語: 英語

skill-quality-review

Review a proposed or existing SKILL.md before publication for practical scope, precise triggers, evidence quality, counterexamples, safety, overlap, composability, and honest lifecycle status. Use when creating, revising, promoting, deprecating, or merging…

原文の言語: 英語

static-analysis-and-type-safety

Use linters, type checkers, security analyzers, and other static checks as evidence while changing a codebase. Use when fixing diagnostics, introducing types, changing analyzer configuration, or deciding whether a warning is real, irrelevant, or dangerous to…

原文の言語: 英語

implementation-planning

Turn verified coding requirements into a minimal, dependency-aware implementation plan with risks, verification evidence, and stopping conditions. Use before multi-file, cross-boundary, risky, or ambiguous AI coding-agent changes.

原文の言語: 英語

skill-composition-and-routing

Select, order, and combine skills for an AI coding-agent task without activating irrelevant, duplicate, or conflicting guidance. Use when several skills may apply, a workflow spans phases, or a skill library is being extended.

原文の言語: 英語

architecture-decision-making

Evaluate and record software architecture choices using constraints, alternatives, evidence, decision boundaries, and reversible consequences. Use when an AI coding agent changes system boundaries, data ownership, integration patterns, concurrency models, or…

原文の言語: 英語

behavior-preserving-refactoring

Restructure code without changing its observable contract. Use when an AI coding agent extracts modules, renames interfaces, reduces duplication, changes control flow, or modernizes implementation while behavior should remain stable.

原文の言語: 英語

concurrency-and-shared-state

Design, debug, or review concurrent and asynchronous code for race freedom, state invariants, ordering, cancellation, resource ownership, and failure recovery. Use when an AI coding agent changes threads, tasks, queues, locks, caches, events, retries, or…

原文の言語: 英語

requirements-to-acceptance

Turn an ambiguous software request into explicit, testable acceptance criteria before an AI coding agent changes a repository. Use for feature requests, bug reports, refactors, migrations, and tasks with hidden assumptions or unclear boundaries.

原文の言語: 英語

data-migration-safety

Design and review database, schema, storage, and data migrations for correctness, compatibility, observability, rollback limits, and safe recovery. Use when an AI coding agent changes persisted data, schemas, indexes, formats, or migration scripts.

原文の言語: 英語

privacy-and-data-handling

Review software changes that collect, use, store, transmit, log, infer, share, or delete personal or sensitive data. Use to reduce privacy risk through purpose limitation, minimization, access control, retention, redaction, and lifecycle-aware design.

原文の言語: 英語

prompt-injection-resistance

Protect AI coding agents from instructions hidden in repository files, issues, documents, web pages, tool output, model context, or memory. Use whenever an agent reads untrusted content, retrieves external context, summarizes data, or proposes an action based…

原文の言語: 英語

safe-git-workflow

Use Git safely while an AI coding agent investigates, edits, reviews, commits, rebases, merges, or publishes repository changes. Use when branch state, uncommitted work, history, conflicts, or destructive commands could affect recoverability or collaboration.

原文の言語: 英語

secure-coding-review

Review an AI-generated coding change for application security risks at trust boundaries, including input handling, authorization, secrets, data exposure, injection, and unsafe failure behavior. Use before accepting security-relevant features, bug fixes, or…

原文の言語: 英語