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概要と使いどころ
Extract self-contained static HTML from a built web application or React components by inlining CSS and images. Use this skill whenever you need to capture a specific UI state, share a static version of a page, or prepare assets for Stitch upload, even if the user just asks to 'save the HTML' or 'mock the view'.
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。
Enhanced dashboard panel component with robust error handling, retry logic, localStorage persistence, and co-located inline helper functions (e.g. timeAgo, statusIcon) for self-contained, import-light panel implementations.
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。
Dashboard panel components (class-based, self-fetching) and push-update sidebar modules (functional, externally-driven) using vanilla TypeScript DOM API, with retry logic, localStorage persistence, and guarded CSS injection.
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。
Comprehensive offensive methodology for software supply chain attacks covering the full kill chain from reconnaissance through exploitation. Addresses dependency confusion across npm, PyPI, and NuGet ecosystems where internal registry override allows an attacker to inject malicious packages that shadow private dependencies. Covers typosquatting techniques for popular packages, compromised package injection via maintainer account takeover or social engineering, and build system attacks through Makefile injection, setup.py install hooks, and npm postinstall scripts. Extends into CI/CD artifact tampering where build outputs are replaced or modified in transit, code signing abuse through stolen or self-signed certificates, upstream repository compromise via commit injection or force-push to trusted repos, and container image supply chain attacks including base image trojaning and registry confusion. Maps to MITRE ATT&CK T1195.001 (Supply Chain Compromise: Compromise Software Dependencies and Development Tools) and T1195.002 (Supply Chain Compromise: Compromise Software Supply Chain). Integrates tooling such as confused for dependency confusion scanning and dependency-check for known vulnerable component detection. Each technique section provides reproducible proof-of-concept patterns, detection guidance for defenders, and engagement-safe execution notes for authorized red team operations.
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。
Cloud security attack methodology covering AWS, Azure, and GCP. Includes credential harvesting (IMDS, ~/.aws, env vars, leaked CI secrets, instance roles), enumeration with cloud-specific tools (pacu, ScoutSuite, Prowler, ROADtools, gcp_enum), privilege escalation paths (IAM PassRole, AssumeRole chains, Lambda/Functions privilege flips, Azure Owner-on-self, GCP serviceAccountTokenCreator), persistence techniques (IAM user/key creation, AAD app registration, GCP svc account key creation, EventBridge/Logic Apps backdoors), data exfiltration (S3/Blob/GCS, snapshot share, RDS/CosmosDB/Cloud SQL exfil), cloud-native lateral movement (cross-account assume, Azure AD multi-tenant, GCP project hierarchy), serverless attacks (Lambda env vars, layer hijack, Step Functions), Kubernetes-on-cloud (EKS/AKS/GKE-specific paths to node and AWS metadata), and CSPM evasion (CloudTrail blind spots, GuardDuty mute, Sentinel rule shaping). Use when the engagement scope is cloud accounts, when you've stolen cloud credentials, or when assessing cloud posture.
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。
Improve academic paper writing quality for ML/CV/NLP-style papers with clear section structure, paragraph flow, and reviewer-facing presentation. Use when drafting or revising Abstract, Introduction, Related Work, Method, Experiments, or Conclusion; polishing figures/tables; checking claim-support alignment; or performing self-review before submission.
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, reviewing, or debugging how an agent's context window gets filled, pruned, or shared — choosing what loads at boot versus on demand, sizing an install or an always-loaded file, fixing an agent that drifts, repeats itself, or forgets constraints mid-task, planning compaction or summarization, deciding single-agent versus subagents, engineering handoffs between agents, or picking a tool loadout. NOT for rewording a prompt's tone, choosing which model to pin, or debugging business logic — those are adjacent moments this skill does not serve. Historical 2024–2025 snapshot; not normative for present-day frontier models — see the Status section.
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 multi-agent topologies that run ON OpenRig — authoring RigSpec and AgentSpec files for new rigs, creating agent startup content (guidance / skills / culture), or diagnosing why a launched rig's agents aren't behaving as intended. NOT for changing OpenRig itself (use developing-openrig); NOT for ordinary CLI operation of an existing rig (use openrig-user). Covers the full authoring lifecycle from user intent to validated, launchable rig.
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。
Turn vulnerability notes, disclosure reports, PoCs, source code, or Codex Security findings into self-contained, sceptically validated, natural-sounding vulnerability reports. Use for one vulnerability or a disclosure campaign; a Codex Security scan is optional.
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 polished dark-themed architecture diagrams as self-contained HTML+SVG files. Use when the user asks for system, infrastructure, cloud, security, or network topology diagrams.
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。
When the user wants to choose between PLG and sales-led, design a sales motion, optimize time-to-first-value, or build a value-before-purchase experience. Also use when the user mentions 'PLG,' 'product-led growth,' 'sales-led,' 'sales motion,' 'free trial,' 'freemium,' 'self-serve,' 'demo-first,' 'time-to-first-value,' 'TTFV,' or 'agent-led sales.' This skill covers sales motion selection, value delivery design, and go-to-market motion architecture. Do NOT use for technical implementation, code review, or software architecture.
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。
Evidence-first pull request judge that reviews a PR and posts one consolidated GitHub review with inline comments via the gh CLI. Runs the repo's own deterministic checks first, researches current official docs before any claim about external libraries or APIs, then reviews correctness, security, structural quality (code judo, spaghetti growth, file-size limits), and AI slop including useless code comments. Every finding must carry evidence, every comment passes a deterministic noise gate before posting, and the verdict (APPROVE, COMMENT, REQUEST_CHANGES) is weighed by findings. Use when asked to review a PR, judge this PR, review this branch or diff before merge, run the-judge, "revise esse PR", "faca o code review", or "julgue esse PR". Do NOT use for reviewing prose or documents, fixing CI failures, resolving merge conflicts, writing the fix itself, or responding to review comments (use gh-address-comments).
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 question, decision, plan, tradeoff, or claim needs a rigorous verdict and one perspective is not enough. Spawns a panel of 3 to 5 subagent jurors that form independent blind opinions, deliberate anonymously under an anti-anchoring and anti-sycophancy protocol, and return one committed verdict with confidence, preserved dissent, and a concrete next action. Domain-agnostic across engineering, architecture, data, product, hiring, strategy, vendor choice, build-vs-buy, and research design. Trigger phrases include "convene a jury", "have agents debate and decide", "get a panel to decide", "multi-agent decision", "stress-test this and decide", "monte um juri", "tribunal de agentes", "painel para decidir". Do NOT use to only critique without deciding (use the-fool for that), to build a plan or write the solution itself, or for simple factual lookups.
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。
Turns decided work — a PRD, design doc, RFC, or thread — into tasks a builder can act on without guessing. Finds slices that each prove something, grounds them in the code, and writes intent, observable criteria with concrete values, the boundary, what the change disturbs, and only the decisions that are hard to reverse. Walks every surface the work exposes and sweeps the nine unwritten requirements, recording each landing as a criterion already in the source, existing behaviour, n/a, or Unresolved — never as a criterion the walk invented. Defaults to one task per source. Use when the user says "write the task", "cut this PRD into tasks", "turn this design doc into work", or "tlc-plan". Do NOT use for discovery itself or to implement — a one-line ticket is a decision; a blank wish is not.
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Feature planning and implementation with 4 adaptive phases (Specify, Design, Tasks, Execute). Auto-sizes depth by complexity. Writes testable requirements in EARS notation, atomic tasks, atomic Conventional Commits, and requirement traceability. Ships deterministic Python validation scripts so structural gates are enforced by code, not memory. Features an independent Verifier (author != verifier, evidence-or-zero), a discrimination sensor, a decision log (STATE.md), a test-coverage matrix, and a self-improving lessons layer. Stack-agnostic and tool-agnostic. Use when (1) planning features, (2) implementing with verification and atomic commits, (3) validating an implementation against a spec. Triggers on "specify feature", "discuss feature", "design", "tasks", "implement", "validate", "verify work", "UAT", "record decision", "pause work", "resume work". Do NOT use for pure architecture decomposition analysis or standalone technical design documents.
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。
test on Windows, enterprise CA, corporate certificate, GPO cert, TLS fetch failed, Windows sandbox, daytona windows, self-hosted cert. Use when validating iPolloWork Windows enterprise TLS/OS-trust fixes in a Daytona Windows sandbox.
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。
do e2e tests, run e2e, validate feature, prove it works, PR proof, frame proof, pnpm evals. Launches iPolloWork on Daytona or local Electron and runs the coded eval flows via CDP. Launch + run mechanics; the proof loop itself is the fraimz 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。
Generate architecture procedurally in Three.js and film it assembling — a small geometry vocabulary that builds pagodas, castles, domes and spires from parameters instead of mesh files, hip roofs with flying eaves driven by a single profile function, and a clipping-plane build animation where everything below a rising line is finished work and scaffolding stands above it. Use for construction studies, architectural title sequences, procedural landmarks, or any hero object that should build itself rather than fade in.
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。
Score a game's combat skills and spells out of 10 with blind judges, then raise the weakest to a bar, for timing and readability (does a skill read as itself, does the blow land when it lands) and for art (detail, light, trails, impact, art direction, judged against a reference such as Diablo IV). Covers filming casts on a stopped, stepped clock, contact strips and close-ups, anonymised two-judge rounds, the levers that moved scores (hero props made from a painted concept with image-to-3D, own schedules and keyed moves, painted additive textures, camera-facing ribbons, light outside the body), the traps that cost points, asset and load budgets, and shipping each skill as its own version. Use when the user asks to score, review, improve, polish or rebuild combat skills, spells or attacks; says a skill "doesn't make sense", "looks bad" or should look like another game; names a target score; or starts a "next skill" thread. Project specifics (review hooks, harness settings, where each skill's code lives, scores so far) go in an optional local references/<project>.md next to this 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。
Build the effects of an ice and frost skill as one visual language in Three.js. Frost that grows like real frost, not a painted fern: a dendritic tree grown on the CPU from a seed point, stems racing outward with barbs and sub-barbs that compete for space in one time-ordered queue, side branches leaned toward their tips, baked into a distance field so the shader draws hairline feathers growing out from the seed with a bright tip, rime, crystal glints and a contact shadow. Faceted ice spikes built from half-spaces and ray-traced against their own planes (Snell, Fresnel, total internal reflection, Beer-Lambert blue, trapped bubbles, a fracture network, frosted edges) that erupt along the frost line, crack along visible planes and shatter into convex pieces that tumble, land on a face and rest, with snow lumps and ice chips settled into the crust. A low freezing mist marched as wind-stretched banks that churn in place and part around the ice, a blizzard column of powder on helical strands with a fibrous veil, glinting diamond dust, and a scanned snow floor with dunes, broken crust, a raking moon with self-shadow and two populations of glints. Use for ice or frost skills, frost novas, ice spikes or walls, glacial eruptions, shattering ice, blizzards, snow devils, freezing mist, frozen ground, "ice and frost", "frost creep", "ice shards", "realistic ice" or "skill effects only". Includes two reusable modules and a five-beat demo on a scanned CC0 snow floor with a failure-mode switch.
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。
Build a production 1:1 stablecoin swap with LI.FI Intents — an interface or backend where the amount the user sends equals the amount they receive (no visible gas or solver fees), same-chain or cross-chain, filled by LI.FI's solver network and verifiable on-chain. USE THIS SKILL WHEN USER WANTS TO: - Build a 1:1 stablecoin swap interface ("send 100 USDC, receive 100 USDC"), same- or cross-chain - Add stablecoin transfers with a guaranteed output amount to an app, backend, or bot - Build enterprise/fintech-grade transfers with no visible gas or solver fees for the end user - Implement the LI.FI Intents quote -> approve -> escrow open -> status loop for stablecoins - Move USDC / USDT 1:1 across chains under an onboarded LI.FI integrator - Understand how the LI.FI integrator ID / Partner Portal enables 1:1 quoting - Use the @lifi/intent TypeScript SDK to quote, build, open, and track an intent order This skill is self-contained and covers the integration end to end via the `@lifi/intent` SDK (the primary path), with the raw `order.li.fi` REST endpoints as a language-agnostic option. For the wider LI.FI product surface (classic swaps/bridges, Composer, Earn), use the `lifi` 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。
Single-agent mission orchestrator. Decomposes a mission into tasks, spawns one worker per task using the default model, verifies exit criteria, and chains tasks with retry. No critic pattern — each worker self-verifies. Simple, fast, works with any model config.
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。
Governs how the OpenBot browser app reads and writes server data — every request goes through `client` in app/src/lib/client.ts, every read is a queryOptions factory in app/src/lib/<entity>/queries.ts, every write is a mutationOptions factory in app/src/lib/<entity>/mutations.ts, and components consume them through useQuery/useMutation. Use when adding or changing a screen that loads server data, calling a /api/... endpoint from the browser, adding a query key, writing a create/update/delete flow, deciding where a fetch belongs, or reviewing a diff that contains the word fetch under app/src. Don't use for server-side route handlers under server/ (that is not browser code), for form validation schemas (those live in lib/<entity>/form.ts), for page layout and Item rows, or for the AG-UI stream itself, which the runtime carries rather than the client.
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。
Wire n8n error handling so failures are loud, structured, and recoverable. Use when building any webhook/API workflow, a scheduled or unattended workflow, or any path where a silent failure would drop user-visible work — and whenever the user mentions error handling, onError, continueErrorOutput, error branches/outputs, retries, retryOnFail, Respond to Webhook status codes, 4xx/5xx, Error Trigger, or "my workflow fails silently". Covers per-node error outputs and wiring, retry/self-healing, error-trigger workflows, and 4xx/5xx response shapes.
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。