「when」の検索結果

Manually distribute working-tree changes (or a batch of edits already made on the current branch) across the correct branches of a Graphite (gt) stack, when gt absorb's blame-based auto-split doesn't match the logical grouping - e.g. new code with no prior line to blame onto, or changes that conceptually belong with a commit gt's algorithm wouldn't pick. Use when asked to "split these changes into the stack", "absorb this properly", "put this fix on the right branch", or when a change was made on top of a branch but really belongs several branches down.

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 a game, engine or modding technique on the open web with archive-aware, rate-limit-safe searches — Wayback Machine/archive.today for dead forums (XeNTaX, Zenhax, old threads), GitHub code/repo search, Nexus/Steam Workshop/Thunderstore APIs, Reddit JSON, YouTube transcripts, plus archive snapshots as evidence. Use when starting work on a game ("how did people mod X before?"), when a documented tool/link is dead, when forum threads are login-gated (semi-auto handoff to the human), and before writing a field note so claims carry source URLs.

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。

Launch, see and drive a real game so an agent can test its own mods. Covers launching via Steam or the exe, windowed mode at a fixed size, screenshots of the game window (GPU-safe capture), clicking and typing into menus, holding keys, relative mouse, crash-reporter cleanup, killing by PID, reading logs, scripted test scenes, and an in-game JSON bridge that lets an agent observe and act. Windows-first (also from WSL), with notes for Linux and macOS. Use when a mod must be verified in game, when navigating menus automatically, or when building a repeatable test or demo scene.

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。

Builds and applies fuzzing dictionaries so a fuzzer can produce the keywords, magic bytes, and tokens a target expects. Covers extracting tokens from source, headers, binaries, and specifications, dictionary syntax, and wiring one into libFuzzer or AFL++. Use when fuzzing a parser, protocol, or file format, when coverage stalls at input validation, or when a target compares against fixed strings.

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。

Designs and improves fuzzing harnesses for C/C++ and Rust. Covers mapping raw bytes onto a target API, generating structured inputs, avoiding non-determinism and false crashes, and deciding what to fuzz together. Use when writing a first LLVMFuzzerTestOneInput or fuzz_target! harness, when a campaign finds nothing or reports crashes that will not reproduce, or when the target API needs structured rather than raw input.

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。

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。

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.

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。

Builds and runs code under AddressSanitizer to catch buffer overflows, use-after-free, and other memory errors during fuzzing or tests. Covers -fsanitize=address builds, ASAN_OPTIONS, reading the crash report, LeakSanitizer, and the overhead and platform trade-offs. Use when fuzzing C/C++ or Rust that has unsafe blocks or FFI, when debugging a memory corruption crash, or when reading an ASan stack trace.

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 threat model for a target codebase. Three modes: "interview" walks an application owner through the four-question framework and produces a threat model from their answers; "bootstrap" derives a threat model from the code plus past vulnerabilities (CVEs, git history, pentest reports) when no owner is available; "bootstrap-then-interview" chains the two when both owner and codebase are present. All write THREAT_MODEL.md in a shared schema. Use when asked to "threat model", "build a threat model", "map the attack surface", or "what should we be worried about in this codebase".

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。

Time-of-Check / Time-of-Use (TOCTOU) race condition exploitation methodology across binary, kernel, filesystem, web, and container layers. Covers symbolic-link races (open/access/stat split), file-descriptor races, fopen/realpath traversal races, /proc and procfs races, FUSE-backed slow-fs races to widen the window, ptrace and signal races, kernel double-fetch / userspace pointer races, container/runc/symlink escape primitives, kubernetes admission/authz TOCTOU, web auth-vs-authz TOCTOU, JWT-claim TOCTOU at gateway vs service, payment/idempotency races, and modern race-amplification techniques (single-packet attack, slow loris, FUSE pause, cgroup freeze, scheduler shaping). Use when you've identified a 'check then act' pattern in code, when fuzzing for race conditions, or when exploiting concurrency bugs in privileged binaries / kernel / orchestrators.

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。

Penetration test and red team report writing methodology. Covers executive summary structuring (risk-led narrative for non-technical readers), technical finding format (title, severity, affected scope, narrative, reproduction steps, impact, remediation, references), CVSS v3.1 / v4.0 scoring with vector justification, OWASP risk rating, evidence hygiene (redacting credentials, hashing client data, time-stamping every action), screenshot and PoC artifact management, finding chain narratives, scope/limitations/assumptions documentation, retest evidence and remediation tracking, deliverable formats (PDF, DOCX, HTML, JSON for SIEM ingestion), client-customer-deliverable separation, and common report mistakes (over-CVSSing, undermining the triager, missing the 'so what'). Use at the end of an engagement when authoring a deliverable, when restructuring a draft for executive readability, or when establishing a reusable report template for a consulting practice.

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 bounded real-world procedure has a deterministic happy path but brownfield, variable, or partially knowable state; when an operation must resume from verified evidence; or when deciding whether agent judgment or ordinary code should own a procedure's control loop.

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 installs mixpanel_headless, pandas, numpy, matplotlib, seaborn, networkx, anytree, scipy (and pyarrow on Python 3.11+), then verifies Mixpanel credentials. It should be invoked when setting up a new environment for Mixpanel data analysis, when dependencies are missing, or when configuring service account or OAuth credentials for the first time.

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。

MANDATORY prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).

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。

Inspect a Dropbox file or folder by checking metadata, shared links, and file content when needed. Use when the user asks what a file is, whether it is shared, when it changed, what access links exist, or asks to read/analyze a Dropbox file.

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 Creative Production is explicitly invoked or mentioned without a concrete request, when the user asks what Creative Production can do, or when the user wants help getting started without enough context to choose a workflow.

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。

Facilitates deliberate skill development during AI-assisted coding. Offers interactive learning exercises after architectural work (new files, schema changes, refactors). Use when completing features, making design decisions, or when user asks to understand code better. Triggers on "learning exercise", "help me understand", "teach me", "why does this work", or after creating new files/modules. Do NOT use for urgent debugging, quick fixes, or when user says "just ship 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。

When the user is a solo founder building their GTM motion, wants to scale without hiring, or needs to design an AI agent team for go-to-market. Also use when the user mentions 'solo founder,' 'one-person startup,' 'solopreneur,' 'bootstrapped,' 'no team,' 'AI agents as team,' 'scaling without hiring,' 'founder-led sales,' 'lean GTM,' 'one-person company,' or 'no employees.' This skill covers the complete solo founder GTM playbook from stack selection through agent team design, revenue-stage transitions, time allocation, and when to finally hire. 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。

MANDATORY prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).

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。

The first security skill to install after setting up OpenClaw — powered by Tencent Zhuque Lab. Works like an antivirus for your AI environment: audits installed skills, scans skills before installation, and performs a full OpenClaw security health check to prevent data leaks and privacy risks. Backed by Tencent Zhuque Lab A.I.G (AI-Infra-Guard). Use when the user asks to start a security health check or security scan for the current OpenClaw environment, such as `开始安全体检`, `做一次安全体检`, `开始安全扫描`, `全面安全检查`, or `检查 OpenClaw 安全`; also use when the user asks to audit a specific skill before installation, review installed skills for supply chain risk, or investigate whether a skill is safe. Do not trigger for general OpenClaw usage, project debugging, environment setup, or normal development requests. Optional cloud mode: set AIG_CLOUD_LOOKUP=off for zero outbound HTTPS; when enabled, only skill_name, source label, and OpenClaw version are sent to A.I.G (never skill bodies, chats, or workspace 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。

Add cursor-following fluid WebGPU distortion over an existing image with the Shaders library's ImageTexture and CursorRipples components. Use when a hero, gallery, or media panel needs a water-ripple mouse effect; when replacing a drifting CSS spotlight or flashlight reveal; or when a prompt says to borrow only the shader interaction from a Shaders.com reference while preserving the current brand, image, copy, and layout.

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 accessible animated AI loading and agent-status indicators with the React thinking-orbs library. Use when a chat, copilot, voice, search, generation, or tool-running interface needs a semantic working, searching, solving, listening, composing, or shaping state; when replacing a generic spinner with an AI activity orb; or when implementing the library's size, theme, speed, pause, reduced-motion, and canvas 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。

Add and tune animated liquid-metal WebGL borders with the React `metal-fx` package. Use when buttons, icon controls, chips, tabs, cards, or selected surfaces need a metallic active, selected, hover, focus, or premium border; when implementing the MetalFx component from metal.jakubantalik.com; or when troubleshooting its presets, themes, strength, glow, reflections, sizing, radius, animation, accessibility, SSR, or performance.

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。