942 件 ・ 関連度順
概要と使いどころ
Day-one data bootstrapping for a new brain. Sequences the highest-leverage data sources to go from empty brain to useful brain in one session. Uses ClawVisor for safe credential handling — the agent never holds raw API keys. Covers Gmail import, calendar sync, contacts seeding, X/Twitter archive, conversation imports, and file archives. Use when a user has just finished gbrain setup and asks "now what?"
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AI image generation with OpenAI GPT Image 2.5, Azure OpenAI, Google, OpenRouter, DashScope, Z.AI GLM-Image, MiniMax, Jimeng, Seedream, Replicate and Agnes APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the user already has multiple prompts or wants stable multi-image throughput. Use when user asks to generate, create, or draw images.
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 a PR against the Pascal architectural rules — package boundaries (core/viewer/editor/nodes), the registry-driven composition model (def.geometry / def.renderer / def.system), legacy-dispatch regressions, the slots + world-scale-UV convention for new nodes/geometry, hook hygiene (useEditor/useScene/useViewer), and selector performance. Use when the user asks to review a PR, audit a branch, or check that changes respect the codebase's 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。
Use when asked to add before/after screenshots of a Lite UI change to a pull request, when a PR touching apps/lite/ui needs its visual change shown, or when extending the screenshot catalogue in apps/lite/e2e/tests/screenshots.spec.ts. Captures both sides locally against seeded fixtures, publishes the surfaces that changed, and posts them to the PR.
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。
Divergent ideation and corpus expansion via Verbalized Sampling plus extended thinking — single-shot generates several internally diverse candidates and surfaces the strongest, or expands a seed corpus into a diverse dataset for evals and test sets. USE WHEN be creative, brainstorm, divergent ideas, creative solutions, maximum creativity, tree of thoughts, radically different, name this, creative angle, expand this corpus, synthetic data, generate diverse examples, create test set. NOT FOR multi-cycle evolutionary ideation with Lamarckian meta-learning (use Ideate).
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Install and onboard a user into LifeOS — the Life Operating System (current state → ideal state via TELOS + the Algorithm). The agentic installer detects your OS + harness, wires hooks with permission, scaffolds your USER tree, pulls in sources you provide, and runs the TELOS / current→ideal interview that seeds your Pulse dashboard. USE WHEN install LifeOS, set up LifeOS, lifeos setup, lifeos-setup, lifeos interview, onboard me, run the interview, integrate LifeOS into my harness, update LifeOS, uninstall LifeOS, first-time setup, lifeos doctor, check my install, what capabilities are broken. NOT FOR building or cutting a LifeOS release (private release tooling), editing TELOS after onboarding (use Telos / Interview), or LifeOS system maintenance (use the private maintenance 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。
Owns the Ideal State Artifact — the primitive holding a project or task's articulated ideal state; scaffolds, interviews, grills a half-formed idea into shape via checkpointed discovery, scores completeness, reconciles feature excerpts to master, seeds from a repo, and appends decisions/changelog/verification across a locked seventeen-section order. USE WHEN ISA, ISC, ideal state, ideal state criteria, project specification, hill-climb, articulating done, fog, not yet specified, grill me, discovery interview. NOT FOR creating new skills (use CreateSkill).
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。
Tunes the CARLA Traffic Manager that drives autopilot vehicles — global and per-vehicle speed, following distance, running lights/signs, lane-change behaviour, lane offset, TM-managed lights, deterministic seed, hybrid physics, and TM sync mode. Use when the user asks to "make the traffic faster/slower", "let cars run red lights", "keep more distance", "stop lane changes", "make traffic deterministic", or otherwise change how autopilot cars behave. For manually driving one car, use control-vehicle.
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。
Capture ideas, tasks, notes, and seeds to their destination
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 a Privacy Impact Assessment in house format for a new feature, product, or processing activity, using the structure learned from your seed PIA. Use when the user says "write a PIA", "privacy impact assessment for", "do we need a PIA for this", "privacy review this feature", or describes a new data processing activity.
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。
Guided customization of your law-student study profile — change one thing without re-running the whole cold-start interview. Adjust current classes, learning style, outline preferences, bar prep subjects, seed materials, or study session cadence. Use when the user says "change my [thing]", "add a class", "update my profile", "new semester", or "customize".
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。
Cold-start setup — learns your jurisdictional footprint and escalation rules from your handbook and termination memos. Asks which states and countries have employees, reads seed documents, and builds a jurisdiction-aware escalation table. Use on fresh install, when CLAUDE.md still has [PLACEHOLDER] markers, or when re-running with --redo or --check-integrations.
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。
Drafts board or committee meeting minutes in your house format. Auto-detects upcoming board and committee meetings from your calendar, asks for the agenda and any slides or pre-read materials, and produces a complete draft in the format learned from your seed minutes. Also handles written consents in lieu of meetings. Trigger: "board minutes", "draft minutes", "upcoming board meeting", "committee minutes", "written consent", or calendar detection of an upcoming board or committee event.
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 an AI impact assessment — structured intake, risk analysis, regulatory classification per regime in scope, policy consistency diff, and recommendation with conditions. Uses the house-style structure learned from the seed impact assessment in `~/.claude/plugins/config/claude-for-legal/ai-governance-legal/CLAUDE.md`. Use when user says "impact assessment for", "assess this AI use case", "run an AIA", "generate an AIA", "we need to document this AI system", "AI risk assessment for X", or follows a conditional triage result.
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。
Prove a Convex feature works — seed, drive as multiple mocked users via convex-test, assert behavior including the negative authz cases (wrong user refused, data-scope enforced).
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。
Rehearse a live-app schema change + backfill on a snapshot-seeded preview deployment, verify, then promote the proven change to prod with the snapshot as rollback.
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。
10 页融资 deck, 白底 + 蓝紫渐变 hero, traction 柱状, $X.XM ask
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Expands one confirmed or suspected vulnerability into a Trailmark graph neighborhood of variant candidates by finding sibling functions, shared callers and callees, common sensitive sinks, common entrypoint paths, interface implementations, override relationships, type/reference neighbors, and structurally similar nodes. Use after one issue is found to seed variant-analysis, semgrep-rule-creator, static-analysis, or manual review with graph-derived candidate locations.
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。
Patches past the barriers that stop a fuzzer making progress — checksum and hash verification, magic-value validation, time-based seeds, and other non-deterministic global state. Covers locating the blocking check, neutering it behind a fuzzing build flag, and avoiding the false positives a patch can introduce. Use when a fuzzer is stuck at validation, when coverage shows large regions behind a checksum, or when valid inputs are impractical to generate.
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Measures and interprets what a fuzzing campaign actually reaches, using llvm-cov, lcov, or a fuzzer's own coverage output. Covers baselining a new campaign, reading coverage reports, and turning uncovered regions into harness, seed, or dictionary work. Use when a fuzzer plateaus, when judging whether a harness is effective, after changing a harness, or when asking why some code is never reached.
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。
Systematic methodology for identifying and exploiting cryptographic implementation weaknesses in real-world applications. Covers padding oracle attacks against CBC-mode ciphers with PKCS7 padding (Vaudenay's original attack through modern padbuster automation), ECB mode exploitation including block cut-and-paste and byte-at-a-time decryption, hash length extension attacks against SHA1/SHA256/MD5-based MACs using HashPump, RSA vulnerabilities including small public exponent, common modulus, Bleichenbacher PKCS1v1.5 padding oracle, and Coppersmith's method for partial key recovery. Addresses weak PRNG exploitation targeting time-seeded generators and Mersenne Twister MT19937 state recovery from observed outputs, timing side-channel attacks against comparison operations, nonce reuse in AES-GCM leading to authentication key recovery, and key derivation weaknesses including insufficient iteration counts and missing salts. Primary tooling includes padbuster, RsaCtfTool, hashpump, and PyCryptodome for building custom exploit payloads. Maps to CWE-327 (Use of a Broken or Risky Cryptographic Algorithm), CWE-328 (Use of Weak Hash), and CWE-330 (Use of Insufficiently Random Values). Emphasizes black-box identification of vulnerable implementations before transitioning to targeted exploitation.
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。
Draws hand-inked cartoon critters and living objects as SVG cards (480x360), each acting out a coding or design idea. Identified by thick black brush-pen outlines that swell and taper and break into separate strokes, loose marker-and-watercolour fills that slip past the line, diagonal hatching instead of solid shadow, huge googly eyes with two specular dots, and stray ink hairs and flecks, in tomato red, leaf green, sunflower yellow, sky blue and teal on white. Covers palette, brush numbers, mascot construction (eyes, blush, mouths, 3-finger hands), props, ground patches and a tested, seeded brush kit. Use when asked for ink sketch, hand-drawn, brush pen, sketchy, doodle, marker or watercolour-cartoon illustrations, googly-eyed mascots or critters, playful empty states, onboarding, 404 or error pages, loading states, blog headers, feature spots or marketing cards.
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。
Draws hand-authored SVG spot illustrations in a halftone line style for design and coding scenes. Identifying traits are a confident black line with a slight hand wobble and contours left open, grey tones made only of fine halftone dots clipped to shapes, a few solid black masses (hair, leaves, a bag, a black tee), warm off-white paper and no colour at all. Covers the two-ink palette, line weights, the open-contour and stipple techniques, a seeded compiler that turns tone tags into dot fills, rubbery long-limbed characters and expressive hands, doodles, floor pools and a render-and-critique loop. Use when someone asks for halftone, stipple, dotted, risograph-like, newsprint, black-and-white, monochrome line, editorial line or zine-style illustrations, or for empty states, onboarding, 404 pages, blog headers, feature spots and marketing cards in that look.
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。
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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。