「cac」の検索結果

Use Hugging Face Evaluate to load metrics, comparisons, and measurements; compute and combine results; run evaluator pipelines; create custom modules; troubleshoot optional dependencies, cache, Hub, and CLI workflows.

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Use the DVC repository skill for data versioning, pipelines, experiments, remotes/cache, metrics/params/plots, Python API usage, and DVC codebase maintenance.

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Use this skill when working with Hugging Face Datasets: loading local or Hub datasets, defining Features schemas, processing/streaming datasets, converting formats, sharing to the Hub, managing cache/offline behavior, or using datasets-cli.

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Fast PARALLEL wiki lookup engine over wiki/techniques + wiki/payloads + wiki/tools + wiki/cheatsheets for a surface/service/vuln-class. Two modes - quick (one qmd search, cheap, fire constantly) and deep (4 parallel subagents, one per area, merged ready-to-use arsenal card, cached). This is the fast path arsenal that `arsenal` delegates to; the hunt-* skills each inline their own qmd_query and can hand off here for a parallel lookup. Use for "what do I use against <surface>", "arsenal for <X>", "deep/full arsenal", "tool + payload + technique + cheatsheet for <X>", "fast wiki lookup", "parallel wiki search", any "how do I attack/exploit <service|vuln-class>" where you want the documented tooling + payloads before hand-rolling.

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CI/CD pipeline attack hunting (GitHub Actions focus) - pwn requests (pull_request_target), script injection, self-hosted runner takeover, cache poisoning, OIDC-to-cloud token theft, poisoned pipeline execution. Wiki-first, FIND schema output.

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Wiki-first "what do I use" lookup - pick the automated TOOL (wiki/tools/), then the PAYLOAD/technique (wiki/payloads/ + wiki/cheatsheets/), for a surface/service/vuln-class BEFORE hand-rolling or working from memory. Use for "tool for <service>", "automated tools for web/<service>", "what should I run on <surface>", "which tool for <X>", "payloads for <X>", "payload arsenal", "cheatsheet for <X>", "how do I exploit <tech/class>", "exploit/attack chain for <X>", "arsenal", any SSRF/XSS/SQLi/SSTI/LFI/JWT/XXE/IDOR/NoSQL/deserialization/CSRF/CORS/CRLF/GraphQL/smuggling/web-cache/OAuth/SAML/MFA/crypto/LDAP/XPath/WebAuthn/file-upload/IMDS/prompt-injection/Modbus ask, plus "privesc arsenal", "CVE arsenal", "default creds", "nuclei templates", "sqlmap/hydra/nmap/bloodhound", "password attacks".

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Link the current Claude Code session to a ticket (Linear, Jira, GitHub Issues, or GitHub Pull Requests) and cache its title/status in karma. Use when the user explicitly asks to link, attach, associate, or connect this session to a ticket, issue, or PR — e.g. "/link-ticket-to-session ABC-123", "link this session to LINEAR-42", "associate this work with issue

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Compress-cache-retrieve huge array tool output.

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Cost discipline for recurring /loop runs. A loop multiplies per-iteration cost by iteration count, so waste compounds. Encodes cache-aware pacing (avoid the 300s dead zone), event-driven-over-polling, a no-change short-circuit, a compact state handle carried between ticks, and a stop condition. Use when a /loop is running or being set up — especially long-lived or frequently-firing ones.

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Cost discipline for recurring /loop runs: cache-aware pacing, event-driven-over-polling, no-change short-circuit, compact state handle, stop condition.

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Guides compliance with country-specific data localization requirements across key jurisdictions including Russia (242-FZ), China (PIPL Art. 40, CAC measures), India (DPDP Act), Turkey, Vietnam, and Indonesia. Covers localization assessment, architecture design, and exemption procedures. Keywords: data localization, data residency, PIPL, 242-FZ, cross-border restrictions.

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Use when making retried HTTP commands or message processing safe against duplicate effects, including database-backed request keys, payload conflicts and concurrent retries. Do not apply caching as a substitute for business idempotency.

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Use when implementing tenant resolution, database or schema isolation, tenant-aware JPA, reactive tenant context, migrations, caching, jobs, or authorization in Spring Boot 3.

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Use when implementing tenant resolution, database or schema isolation, Hibernate 7 tenancy, reactive tenant context, migrations, caches, jobs, or authorization in Spring Boot 4.

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Geracao de imagens humanizadas via Google AI Studio (Gemini). Fotos realistas estilo influencer ou educacional com iluminacao natural e imperfeicoes sutis.

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Owns repositories, data sources and mapping for Compose and Compose Multiplatform apps: DTO to domain to UiModel boundaries, Ktor clients and bearer auth, WebSocket and SSE, Room, DataStore, Paging 3, offline-first and data-layer tests. Use when writing or reviewing a repository, data source, DTO, mapper, HttpClient, bearer token, WebSocket, SSE, Room DAO, migration, DataStore, PagingSource, RemoteMediator, offline cache or MockEngine test. Do NOT use for ViewModel or UI wiring (compose-feature), composables (compose-ui), the MVI contract or error tiers (compose-architecture), Gradle modules (compose-project), or commonMain vs platform splits (compose-platform).

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Rigid body, soft body, and destruction setups in Blender including constraints, cell fracture debris, and baked caches via MCP.

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Garment blockout, cloth simulation, pin groups, and bake to shape keys or caches for characters and props in Blender via MCP.

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Detect known malicious npm package versions and install-time supply-chain indicators in repositories, lockfiles, and node_modules. Use when a user mentions an npm compromise, Shai-Hulud, the Keyv/cacheable incident, suspicious preinstall scripts, credential-stealing packages, or asks whether a JavaScript project was exposed to a package supply-chain attack. Do not use as a general CVE or license audit.

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Plan, diagnose, and verify performance budgets, latency targets, load tests, capacity estimates, bottleneck analysis, caching strategy, query efficiency, queue throughput, and regression gates. Use when a feature may be slow, a system must scale, a performance regression is suspected, or release readiness depends on throughput, cost, memory, CPU, or response time.

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Before removing code that looks redundant, wasteful, or "wrong" based on log observations or code review instinct, grep the test suite for tests whose names describe the exact behavior you're about to remove. A test whose title literally describes the "weird" thing (e.g. "calls play() even when player reports playing", "retries even when first call succeeded", "skips cache even when fresh") is a load-bearing workaround, and the production log you're reacting to is probably the workaround working as designed — not a bug. Use when: (1) Tempted to remove a function call that "looks redundant" based on a production log, (2) About to "simplify" a conditional that seems to always take the same branch, (3) Reviewing code and thinking "why would anyone write this?", (4) A log line shows a state that seems impossible or contradictory (e.g. playing=true but positionMs=0 for seconds), (5) CI surfaces a failing test whose name describes exactly what your fix removed.

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Fix Riverpod "Using ref when widget is unmounted is unsafe" error in ConsumerStatefulWidget. Use when: (1) Error thrown in dispose() when calling ref.read(), (2) Need to call a service method during widget cleanup, (3) ConsumerStatefulWidget needs to perform actions on provider when user navigates away. Solution: cache the service/notifier in initState with late final.

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Fix Node.js pg-pool not reconnecting after CloudSQL connection reset (ECONNRESET). Use when: (1) Retries fail with same ECONNRESET error despite retry logic, (2) "Connection terminated" or "connection already closed" errors persist across retries, (3) "remaining connection slots are reserved" errors in CloudSQL, (4) High-concurrency Node.js app with pg-pool and CloudSQL/managed PostgreSQL. The pool caches dead connections - you must recreate the pool, not just retry.

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Fix deployed Rust binaries not reflecting code changes when using docker buildx. Use when: (1) Code changes are verified locally (tests pass) but production behavior doesn't change after deploy, (2) Docker image has a new tag but contains old compiled binary, (3) Dockerfile uses multi-stage build with dependency pre-compilation layer and source copy layer. Docker buildx layer caching can serve stale compilation output even when source files change, especially with cross-compilation (--platform linux/amd64).

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