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概要と使いどころ
Digital signatures and time stamps through Openapi - eIDAS-compliant electronic signatures (SES with OTP, qualified QES, e-seals, massive automatic signing) and qualified time stamping of documents. Use when the user needs a document signed electronically or time-stamped with legal validity.
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 common CLI, Kali, and shell tools to inspect web Targets, captured assets, logs, HAR/PCAP files, packages, and generated artifacts, then turn observations into Evidence, Security Signals, and ranked Hypotheses. Use when the user asks for web bug-hunting inspection, digital records, URL maps, crawling, endpoint discovery, JS bundle/package analysis, light forensics, or command-line investigation 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。
Implement authentication and authorization with Better Auth - a framework-agnostic TypeScript authentication framework. Features include email/password authentication with verification, OAuth providers (Google, GitHub, Discord, etc.), two-factor authentication (TOTP, SMS), passkeys/WebAuthn support, session management, role-based access control (RBAC), rate limiting, and database adapters. Use when adding authentication to applications, implementing OAuth flows, setting up 2FA/MFA, managing user sessions, configuring authorization rules, or building secure authentication systems for web applications.
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Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
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Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
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。
Execute a swarm plan wave by wave with filesystem isolation via git worktrees. For each wave, main context decides the scenario recipe + implementation contract for every task, then spawns one swarm-worker per task in parallel. Each worker executes its recipe and reports JSON status. Worktree merge-audit verifies write-set discipline before changes land on main. Aborts remaining waves on any audit or task failure.
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On-demand "what's new" generator. Main context (which knows the impending change) writes keepachangelog-style entries to a fragment file at `.claude/state/whatsnew/<slug>.json` (gitignored, transient). An optional per-project `project.json → whatsnew.route_workflow` names a routing workflow that consumes the fragment and renders it wherever the project wants (a site page, a release body, a docs page). Not a workflow phase — it never blocks a commit, and it never writes `CHANGELOG.md` (that file is owned solely by semantic-release in CI).
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。
Write technical documentation that matches the measured profile of professionally-written docs. Carries the craft rules derived from 114k words of pre-2022 documentation (SQLite, PostgreSQL, Python, Django, Rust, Go, Pro Git, nginx), the per-Diátaxis-type numeric targets, and the copywriting register for page leads. Use when drafting or rewriting any documentation prose, or when a page is factually right but reads as machine-written.
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。
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer
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Workflow Phase 11 — Commit Preparation and Execution. Stages and commits the work. Requires `/grant-commit` first (Git Commit Guard enforces a 15-min ad-hoc consent window; inside a workflow the grant is slug-scoped and one grant covers the whole landing).
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。
Power batch-sprint helper — hosts the two behaviours that distinguish the `power` workflow track from spec-entry. (1) Per-ticket iteration: security runs once PER TICKET over workflow.json.tickets[] while the mechanical phases run once for the batch. (2) Commit split: at the commit phase, group the batch's working tree into ordered Conventional Commits via commit-split.mjs, closure last. Invoked within a `power`-track workflow; not a standalone feature. Opt-in via velocity.power_mode.enabled; requires git.
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。
npm 릴리스 운영. release 브랜치 선택 커밋 반영, 검증, publish, tag, GitHub Release, main 동기화 진행
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。
남은 작업과 다음 작업 선정. .progress/.docs/현재 git 상태 기반 우선순위 정리
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。
Sets up an `## Agent skills` block in AGENTS.md/CLAUDE.md and `docs/agents/` so the engineering skills know this repo's issue tracker (GitHub or local markdown), triage label vocabulary, and domain doc layout. Run before first use of `to-issues`, `to-prd`, `triage`, `diagnose`, `tdd`, `improve-codebase-architecture`, or `zoom-out` — or if those skills appear to be missing context about the issue tracker, triage labels, or domain docs.
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 a detailed refactor plan with tiny commits via user interview, then file it as a GitHub issue. Use when user wants to plan a refactor, create a refactoring RFC, or break a refactor into safe incremental steps.
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。
Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".
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。
Clone/create/fork repos; manage remotes, releases.
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。
Review PRs: diffs, inline comments via gh or REST.
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 maximize GitHub repo quality from 7/10 to 10/10 perfect
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。
Generate user-facing App Store release notes from git history since the last tag.
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。
Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.
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。
Verify every claim from multiple independent angles -- code, docs, tests, git history. Use when you need high confidence that something is correct before acting on it.
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。
Trace the purpose of suspicious legacy code using current callers, tests, and Git history when deciding whether it can be changed or removed.
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。
Write meaningful Replica tests. Use when adding regression coverage, testing new behavior, or changing existing automated tests.
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。