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Use when checking skill quality, validating frontmatter before commit, or diagnosing validator warnings. Runs the plugin validator on a skill, agent, or plugin directory — reports token complexity, broken links, frontmatter issues, and structural problems. Pass the path as an argument.

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。

Add automated documentation updater to any Claude skill. Creates a Python sync script that downloads upstream docs, processes markdown for AI consumption, and maintains local cache with configurable refresh. Collects template variables, then delegates implementation through 5-phase workflow. Use when adding auto-updating reference documentation to plugins or skills.

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。

Agent Skills portable format router. Use when creating cross-client skills, validating portable frontmatter, packaging skills for upload or API use, or implementing skill discovery and activation.

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。

Use when creating a new skill or updating an existing skill that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Activates on "create a new skill", "add a skill to plugin", or "update existing skill".

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。

Analyze and improve processes or systems using an explicit semantic model, adaptive instruction resolution, evidence-driven change, and claim-level validation. Use when reviewing process quality, resolving ambiguity or gaps, deciding how much procedural detail is necessary, or validating consequential process behavior.

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。

Audit skill lifecycle by tracing call chains, detecting circular dependencies, finding instruction contradictions, identifying duplicated datasets, analyzing bidirectional coherence, discovering scriptable sequences, and learning patterns. Use when checking skill coherence, validating skill workflow, finding semantic gaps in plugin structure, or auditing plugin before marketplace submission. Generates audit reports to .plugin-creator/audits/ with findings by dimension.

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。

Audit agent lifecycle — validates agent execution capability against configuration. Accepts plugin path, runs 8 semantic audits (capability vs config alignment, skill loading correctness, inter-agent contracts, prompt contradictions, tool sufficiency, dead agents, scriptable patterns, pattern learning), writes reports to .plugin-creator/audits/. Use when auditing agent lifecycle, checking agent capabilities, verifying tool access, finding dead agents, validating agent contract alignment, or confirming agents can execute workflows.

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。

Use when creating or updating the project skill discovery config — generates or regenerates .dh/skill_discovery.yaml by scanning the repo to infer tech stack, inventorying installed skills via npx skills list, loading candidate skill content before suggesting, and writing a config-driven skill injection file. Triggers on /dh:setup-skill-discovery invocations and programmatic --auto calls from add-new-feature Phase 3.

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。

Use when designing or validating GitLab CI/CD and automatic release lifecycles, inspecting pipeline and job state read-only, composing non-interactive merge-request commands, or setting up protected refs and release credentials. Also covers reusable components and inputs, GitLab Functions, gitlab-ci-local, glab command composition, and GitLab Flavored Markdown. For pipeline execution or retry requests, provide inspection and command drafting while leaving execution to the operator.

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。

Use when installing, updating, or troubleshooting the dasel v3 binary — runs the install script, verifies installation, and diagnoses PATH and download issues

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 and iterate on the SDLC Layer Separation Architecture implementation. Runs validation checks (cross-references, doc completeness, layer metadata, integration points), produces a findings report, and supports iterative fixes. Use when validating first-pass implementation, before claiming layer work is complete, or when improving layer docs/schema.

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。

Manages feature implementation task state via SAM MCP tools. Use when querying task status, listing ready tasks, dispatching tasks for execution, updating task timestamps, or coordinating multi-task feature rollout. Activated by the /dh:execution orchestrator to track progress — also activates directly when managing tasks.

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。

Badge design and selection knowledge base for shields.io badges in README files. Use when writing or updating READMEs, choosing badge layouts, selecting badge styles, adding project health indicators, or picking Simple Icons logo slugs. Covers shields.io URL encoding rules, static vs dynamic badge selection, style variants (flat/flat-square/for-the-badge/social/plastic), layout patterns (two-tier/inline/centered), project-type badge sets for Python/JS/Rust/Claude plugins, color reference, non-obvious logo slugs, and common anti-patterns to avoid.

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。

Validate and iterate on the SDLC Layer Separation Architecture implementation across the check categories defined in its Evaluation Checklist — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.

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Manages the research/ knowledge base of verified tool and library research entries via knowledge-explorer.py. Commands — list (browse by category or SDLC layer), fetch-github (draft entry from GitHub repo), add (validate and route to category dir), update-append (append dated revision), generate-descriptions (repair missing or low-quality descriptions), migrate (convert inline-header entries to frontmatter). Use when browsing KB topics, adding or updating research entries, fetching GitHub repo metadata, generating descriptions in parallel via Haiku subagents, or migrating old-format entries to skill-spec frontmatter.

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。

Bulk-refresh research entries in ./research/ using parallel research-curator agents. Use when /refresh-research is invoked, stale research needs updating, or bulk re-verification of research entries is requested. Inventories entries by review date and age, runs RT-ICA pre-flight, spawns agents in waves of 5, then validates, reviews, and commits through research-curator. Supports --all, --stale, --category, --layer, and --dry-run flags.

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。

This skill should be used when the user asks to "update the changelog", "add a changelog entry", "write a CHANGELOG entry", or when preparing a git commit in StreamFlow that requires updating the `## [Unreleased]` section of `CHANGELOG.md`.

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。

Use when converting an agentic repository into a ChatGPT/Codex Plugin or when building, repairing, validating, packaging, submitting, publishing, or auditing an existing Plugin.

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。

Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.

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。

Advises on setting up and validating LLM text classification: codebook, learning regime and model choice, prompts, validation against human coding (kappa, F1), hybrid review, measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline. Use to classify, code, or label text at scale; category discovery goes to topic-modeling.

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。

Use when validating indexed arguments

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。

Use when validating vague skill names

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。

Use when validating deep references

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。

Use when validating windows paths

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。