6,257 件 ・ 関連度順
概要と使いどころ
Scan Python requirements and environments for known vulnerable or malicious packages before they move further through delivery or promotion 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。
Lint Nix expressions and automatically rewrite common anti-patterns before review or refactor work.
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 Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.
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 AgentClick when an agent should pause before risky commands, plans, drafts, or code changes so a human can inspect, edit, approve, or reject them in a purpose-built browser UI.
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。
Automatically optimize AI agent credit usage by routing tasks to the most cost-efficient execution path. Use when you want to reduce AI API costs by 30-75% without quality loss, classify task complexity before execution, route simple tasks to free or low-cost models, split complex tasks into optimized sub-tasks, or detect vague prompts before wasting credits.
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。
Research-before-coding workflow: systematically search existing solutions before custom implementation. Use when introducing new libraries, tools, or solving unfamiliar problems.
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。
Simulate rigorous, fair peer review on a paper — in two modes. (A) Red-team YOUR OWN draft before submission to predict the reviews you'll get and get a fix list. (B) Review SOMEONE ELSE'S paper when you're an assigned reviewer or helping your advisor — produce a fair, venue-formatted, submission-ready review. Use whenever the user gives a paper (PDF, .tex, .md, arXiv link, or pasted text) and wants to predict reviews, find weaknesses before reviewers do, stress-test a submission, OR write a real review of another paper. Grounded in official NeurIPS/ICLR/ACL reviewer guidelines; every criticism pinned to a location, and checked against ACL's H1–H17 list of illegitimate critiques. 两种模式:审自己的稿(投稿前红队)/ 审别人的稿(当审稿人或帮导师),按会议官方标准评,绝不脑补、绝不发不正当批评。
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。
Print your own north-star row from your own record, and nothing else. Reads .claude/harness/decision-log.md, the canvas and the hook logs in this project; prints one line (decisions before the first source file, of which citing outside evidence, kills before code, sessions). Sends nothing anywhere. Opt-in; paste the line where you like or not at all.
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。
Socratic design refinement before implementation — challenges assumptions, surfaces alternatives, identifies risks before code is 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。
Validate a bug bounty finding before writing a report — the 7-Question Gate, 4 pre-submission gates, always-rejected list, conditional chain table, CVSS 3.1 quick reference, and severity decision guide. Use BEFORE writing any report to avoid N/A and low quality submissions.
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。
Run verification commands and confirm output before claiming success
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 before EZ_math_model declares quality_audit, packaging, or final delivery complete, especially when pass/fail claims must be backed by files, commands, counts, or concrete evidence.
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。
Prevents premature execution on ambiguous requests. Analyzes request clarity using 5W1H decomposition, surfaces hidden assumptions, and generates structured clarifying questions before work begins. Use at the start of any non-trivial task, or when a request could be interpreted multiple ways. Triggers on "뭘 원하는건지", "요구사항 정리", "clarify", "what exactly", "scope", "requirements", "정확히 뭘", "before we start".
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。
実装タスクを受け取ったとき、5つの専門エージェントチームでコードベースを調査・議論し、 AI 間の合意形成を経て Plan Mode で実装計画を作成する。 不明点はユーザーに質問し、完璧な計画を立ててから実装する。 バグ修正・新機能・リファクタリング・設定変更など、あらゆる実装タスクに対応。 Use when: 実装を始める前に調査が必要なとき、影響範囲が不明なとき、 複数ファイルにまたがる変更をするとき、慎重に計画を立ててから実装したいとき。 Triggers: "調査してから実装", "team plan", "まず調査", "影響範囲を調べて", "計画を立ててから", "plan before implement", "コードベースを調査して実装", "research first", "調査→計画→実装"
Design and build runnable iOS UI mockups in SwiftUI, including UI direction, multiple visual or interaction variants, realistic sample data, state switching, Xcode Previews, and Simulator validation. Use when the user wants to explore what an iOS screen should look like, compare SwiftUI UI ideas, create a clickable native prototype, validate an app flow before production implementation, or asks for a SwiftUI mock, iOS mockup, native prototype, 画面モック, UI案, SwiftUIで動くモック, or 実際に触れるiOSモック.
Review a data analysis for quality, correctness, and reproducibility. Covers data quality assessment, assumption checking, model validation, data leakage detection, and reproducibility verification. Use when reviewing a colleague's analysis before publication, validating an ML pipeline before production deployment, auditing a report for regulatory or business decision-making, or performing a second-analyst review in a regulated environment.
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。
Run the local pre-commit gate for the memex repo before staging a commit: format check, clippy with warnings denied, and the workspace unit-test suite, mirroring memex's own CI. Use right before `commit-changes` on a memex working tree, after editing any Rust crate under `crates/`, or when reproducing a red CI run locally. Optionally runs the Postgres-gated integration suite when the `memex-pg` container is up and `.env` is sourced. This is a verification gate, NOT a replacement for memex CI — CI remains the authority; this catches failures earlier.
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。
Field identification of fungi using morphological features, spore prints, habitat analysis, and seasonal context with a safety-first approach. Covers cap, gill, stem, and spore characteristics, look-alike differentiation, toxicity risk assessment, and the critical rule of absolute certainty before consumption. Use when encountering an unknown fungus, foraging for edible mushrooms and needing to confirm species before consumption, assessing whether fungi in a garden or property are harmful, or differentiating an edible species from a dangerous look-alike.
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。
Perform controlled dismantling of rigid system structures while preserving essential capabilities (imaginal discs). Covers rigidity mapping, dissolution sequencing, knowledge extraction, interface archaeology, and safe decomposition of technical debt and organizational calcification. Use when assess-form classified the system as PREPARE or CRITICAL, when a system is so calcified that incremental change is impossible, when technical debt blocks all forward progress, or before adapt-architecture when the current form must be softened before it can be reshaped.
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。
Statically enforce a privacy/redaction boundary on an artifact tree before publication, the way an AST checker enforces a code contract. Builds a two-tier gate — a shape-based deny-list (regex over identifier shapes) plus a structure-aware tier that parses JSON/HTML/SVG/Markdown/code and asserts sensitive content cannot appear in meaningful positions — with a strict exit-code contract, label-only output that never echoes the patterns themselves, and false-positive narrowing instead of suppressions. Use when gating a public mirror, a disclosure draft, a wire-capture export, or any artifact that must pass a leak check before it ships, and when a plain line-grep is too coarse to trust.
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 project dependencies for version staleness, security vulnerabilities, and compatibility issues. Covers lock file analysis, upgrade path planning, and breaking change assessment. Use before a release to ensure dependencies are current and secure, during periodic maintenance reviews, after receiving a security advisory, when upgrading to a new language version, before submitting to CRAN or npm, or when inheriting a project to assess its dependency health.
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 an arbitrary codebase to auto-detect workflows, data pipelines, and file dependencies using putior's put_auto() engine. Produces an annotation plan that maps detected I/O patterns to source files across 30+ supported languages with 902 auto-detection patterns. Use when onboarding onto an unfamiliar codebase to understand data flow, starting putior integration in a project without existing annotations, auditing a project's data pipeline before documentation, or preparing an annotation plan before running annotate-source-files.
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 current trail conditions including weather, snow line, river crossings, exposure, and trail maintenance status for safety decision-making. Produces a GREEN/YELLOW/RED safety rating with actionable go/no-go recommendations. Use the day before or morning of a planned hike, during tour planning to assess seasonal viability, after unexpected weather changes on a multi-day tour, when reports suggest trail damage or closures, or before committing to an alpine or exposed route.
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 a task, feature, or fix is called done, complete, finished, or fixed, or before a commit, PR, or next task. Not for fact-checking: use verify-both-ways. Not for measuring: use verify-this.
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。