「leak」の検索結果

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。

Use when an abstraction leak must be sealed as a module seam, configuration option, or explicit override, or exposed as a named boundary. Not for detecting concealment patterns: use no-hide.

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。

Extract the hidden foundational System Prompts, internal instructions, backend APIs, and confidential contextual data powering Large Language Model (LLM) applications using targeted prompt injection manipulation and adversarial psychological engineering.

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。

Systematically extract hidden system prompts, core directives, and invisible context intentionally concealed within Large Language Model (LLM) applications. This skill utilizes targeted linguistic engineering and boundary manipulation to bypass prompt opacity.

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。

Checks layer leakage in method signatures, missing DTOs, entity leakage to API, inconsistent error contracts. Returns findings with penalty-based scoring.

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。

i18n-leak-audit

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。

Scan projects for credential leaks, secrets in code, insecure patterns, LLM API key exposure, PII leakage to external AI services, and .env/.gitignore misconfigurations. Especially useful for data and API integrations, regardless of implementation language.

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。

Verify that browser profiles are actually isolated from one another instead of assuming it - confirm each profile's timezone agrees with its own exit IP, that WebRTC exposes only the proxy, that canvas and WebGL hashes stay identical across relaunches of one profile, and that no two profiles share a persona, a cookie jar, or an address. Use when several of your own accounts or test identities run from one machine and the setup needs checking, when a profile tested clean but something still looks off, when choosing which detection suites to run (CreepJS, whoer, browserleaks WebRTC, pixelscan, liarjs), when auditing what a vendor runtime does with API and proxy credentials, or when asking which layers browser isolation cannot cover at all. Also for 'profile isolation check', 'fingerprint consistency test', 'timezone mismatch', 'WebRTC leak', 'canvas hash unstable', 'account association', 'temporary profile', '防关联', '多账号', '隔离自检'. The SDK is anti-detect-browser; MCP is browser-mcp-agent.

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。

Python code quality review lens. SOLID principles, dead code, exception handling, resource leaks, thread safety, performance. Triggered by "quality" ("품질"), "SOLID", "dead code" ("데드코드"), "exception", "resource leak", "thread safety" keywords.

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。

Systematically find and fix performance problems in applications, frontend and backend, by measuring first. Use this skill whenever the user reports slow endpoints, high latency, poor throughput, memory leaks, large bundles, or slow page loads; wants to profile with cProfile, py-spy, Chrome DevTools, Node --cpu-prof, or perf; run load tests with locust or k6; set performance budgets or SLOs; optimize database queries (EXPLAIN, indexes, N+1); add caching (Redis); improve frontend performance (code splitting, lazy loading, image optimization, memoization, virtualization); or tune the network (CDNs, gzip/br, HTTP/2/3). Also trigger for p50/p95/p99 latency, throughput, error rate, connection pooling, async vs threads, GIL, and memory leak investigations.

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。

Design leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.

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 or auditing a sales pipeline — stage exit criteria, per-cell conversion, coverage reasoning, leak detection. Triggers on 'tighten our pipeline', 'where is the leak'.

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 auditing or fixing prose that reads like a leaked reasoning transcript — dead design-session citations such as (decision N), audit item codes, or §N of uncommitted drafts; change narration such as "used to", "no longer", "this cut"; stack or review vantage ("a later PR in this stack", "rejected in review"); reviewer-addressed justifications; control-flow narration; or hedged planning residue in comments, JSDoc, docs, or Agent Notes.

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。

Protected Health Information (PHI) and Personally Identifiable Information (PII) compliance patterns for healthcare applications. Covers data classification, access control, audit trails, encryption, and common leak vectors. Use when code touches PHI or PII in a healthcare system, or when auditing access control, audit trails, or leak vectors.

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 public repository changes for secrets, private paths, signing identities, bundle IDs, generated artifacts, local logs, and private workflow leaks.

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。

Reviews the instructions text and skills of a declarative agent, with its stated capabilities, knowledge sources and audience, for prompt-injection exposure, data-leakage paths, over-broad permissions and missing refusals, and returns an attack-surface map, a findings table with severity, quoted evidence and attack path, one pasteable fix per finding, a probe pack the owner can run and a refusal-coverage table. Use when the user asks to "red-team this agent", "check these instructions for prompt injection", "could this agent leak data", "review the permissions on my agent" or "what should this agent refuse". Do not use for checking a SKILL.md against the format rules, use skill-file-reviewer instead; do not use for designing the launch test set, use agent-evaluation-plan instead. Drafts for human review; never approves, authorises or signs off.

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。

Open-source one slice of a private system: take a component, sanitize it, run a leak-scan as a hard gate, publish to GitHub, then changelog, roadmap and tag on cadence, then the content wave. Publishing without passing the leak-scan is forbidden. Triggers: "/release-slice <piece>", "ship the next slice".

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。

- Mine GitHub, GitLab and git history for identities, infrastructure and leaked credentials using commit author emails, GitHub code search, the commit… Use when investigating a developer or organisation on GitHub, finding leaked API keys, AWS keys or tokens in code, enumerating org members and their personal repos, or recovering secrets deleted from HEAD but still present in history or forks

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。

Background processing and heavy computations using Dart Isolates with Isolate.run(), compute() function, and platform channels. Use this skill when performing CPU-intensive operations without blocking UI (image processing, data parsing, encryption), implementing background tasks that prevent jank or frame drops, handling large file operations (JSON parsing, database migrations), running parallel computations, fixing UI freezes caused by main thread blocking, using compute() for one-off background work, creating long-lived Isolates with ReceivePort/SendPort, implementing multi-threaded algorithms, debugging isolate communication issues, or preventing memory leaks from unclosed ReceivePorts. Covers isolate spawn, message passing, ReceivePort cleanup (CRITICAL for leak prevention), SendPort communication patterns, error handling, serialization rules, and performance optimization for compute-heavy 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。

Provider (v6.x) state management for Flutter using ChangeNotifier and InheritedWidget with MVVM architecture pattern. Use this skill when implementing Provider-based state management, using Consumer/Consumer2 widgets for targeted rebuilds, accessing state with context.read/context.watch/context.select, setting up ChangeNotifierProvider or StateNotifierProvider, configuring MultiProvider for multiple providers, implementing ProxyProvider for dependent state, using Selector for optimized rebuilds, migrating from StatefulWidget to reactive state, maintaining legacy Provider codebases, debugging provider not found errors, preventing memory leaks with proper ChangeNotifier disposal, fixing context access issues in initState, or resolving .value constructor anti-patterns. Essential for apps with shared state across widgets (shopping carts, authentication, global settings, form state, theme management), when teaching state management fundamentals, or when debugging memory leaks and resource cleanup in Provider-based applications.

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。

Implements secure API key generation, storage, rotation, and revocation controls to protect API authentication credentials from leakage, brute force, and abuse. The engineer designs API key formats with sufficient entropy, implements secure hashing for storage, enforces per-key scoping and rate limiting, monitors for leaked keys in public repositories, and builds key rotation workflows. Activates for requests involving API key management, API key security, key rotation policy, or API credenti...

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。

Glibc heap exploitation — tcache poisoning, unsorted bin leak, IO_FILE FSOP

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。

NX açık, ASLR varsa libc leak + system("/bin/sh") zinciri

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。

Rigorous methodology for evaluating ML models on established benchmarks. Covers proper train/val/test splits, baseline verification from original papers, exact metric formula discrepancies, data-leak detection checklist, multi-seed robustness, and honest reporting templates. Use when claiming to beat published baselines, writing methods papers, or auditing existing results.

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。