710 件 ・ 関連度順
概要と使いどころ
Test APIs for authentication and authorization bypass vulnerabilities including JWT manipulation, OAuth2 flaws, API key leakage, broken authentication, and token forgery. Use this skill when assessing REST/GraphQL APIs for access control weaknesses, session management flaws, or credential handling issues. Covers JWT algorithm confusion, OAuth redirect manipulation, and rate limit bypass.
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。
Reactive programming with RxJS - Observables, operators, Subjects, error handling, and memory leak prevention
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。
React Native performance profiling, optimization, and monitoring - JS/UI thread analysis, re-render prevention, list optimization, image performance, bundle size, startup time, memory leaks, React Compiler, New Architecture benefits
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。
Backend code review patterns. Use when reviewing API routes, database operations, auth middleware, and server utilities. Covers injection, boundary validation, authorization coverage, secret/PII exposure, error leakage, and query patterns.
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。
Close out a review/meta branch that lands an evidence-ingest / curation batch: create the named nodes, ingest verified evidence, appraise Trust Magnitude, calibrate stars, wire suites, correct upstream naming, stage ONLY the intended artifacts (dropping CRLF churn and blocking leaks), validate, and open the PR. Use when a curation/ingest batch is verified and ready to land, or when the user says "close the review/meta branch", "land the ingest batch", "wrap up the intake PR", "/gaia-review-meta-close". Wraps the deterministic hygiene in scripts/review_meta_close.py and gates the judgment calls (calibration, Origin, suite promotion, naming, merge) behind explicit human checkpoints.
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 this skill for Phase 3 of the evidence verification pipeline — the adversarial audit step. Use when you need to check the evidence data lake for bad data before ingestion: dead links, wrong URL formats (tree/ vs blob/), subjective wording ("elite", "high-quality"), stale migration notes, benchmark catalog misuse/vendor-claim leakage, or skills whose star evidence conflicts with classified evidence level. Triggers on phrases like: "audit the data lake", "adversarial check", "ev-adversarial-audit", "check for noise in evidence", "flag bad evidence", "run the audit phase", "quality check the by-type files", or any reference to Phase 3 of the pipeline.
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 provides expert-level guidance for debugging and fixing bugs in VS Code extensions. Use when investigating runtime errors, fixing memory leaks, resolving WebView issues, debugging activation problems, fixing TypeScript type errors, or troubleshooting extension communication failures. Covers systematic debugging workflows, common bug patterns, root cause analysis, and prevention strategies.
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 soak test protocol for extended play sessions. Defines what to observe, measure, and log during long play sessions to surface slow leaks, fatigue effects, and edge cases that only appear after sustained play. Primarily used in Polish and Release phases.
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。
Unity async and coroutine correctness patterns. Catches common mistakes with Awaitable double-await, missing cancellation tokens, thread context after BackgroundThreadAsync, coroutine error swallowing, batch mode WaitForEndOfFrame, and Addressables handle leaks. PATTERN format: WHEN/WRONG/RIGHT/GOTCHA. Based on Unity 6.3 LTS documentation.
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 diagnosing or improving frontend performance, Core Web Vitals, bundle size, runtime rendering cost, network waterfalls, memory leaks, long tasks, Lighthouse findings, or performance budgets; Chinese triggers include performance optimization, page freeze, slow first screen, bundle volume, Web Vitals.
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。
Natural-language orchestration of security tools through Gemini. Translates intent ("scan this scope for exposed admin panels") into safe, parameterized tool pipelines using nmap, masscan, naabu, httpx, nuclei, ffuf, gobuster, subfinder, amass, dnsx, katana, gau, semgrep, trivy, checkov, gitleaks, syft, grype, and custom scripts. Use when the user wants Gemini to drive a chain of CLI tools end to end with guardrails.
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。
OSINT and reconnaissance automation skill for passive collection, pivoting, deduplication, and graph-based analysis using subfinder, amass, dnsx, chaos, crt.sh, github-dorks, waybackurls, gau, gitleaks, trufflehog, theHarvester, h8mail, holehe, sherlock, Shodan, Censys, FOFA, and Hunter. Use for asset discovery, exposure monitoring, and authorized reconnaissance only.
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。
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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 task needs the judgment of an Energy Auditor — running a blower-door/duct-leakage diagnostic sequence on a home or light-commercial building, testing combustion appliances for worst-case backdraft risk before recommending air sealing, reconciling utility billing history against walkthrough findings, ranking retrofit measures by savings-to-investment ratio (SIR) for a weatherization or utility rebate program, or writing the prioritized scope of work an install crew executes without the auditor on site. Distinct from an energy-engineer (industrial/commercial capital planning, IPMVP measurement and verification, calibrated simulation) — this role owns the field diagnostics and the residential/light-commercial audit-to-scope handoff.
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 task needs the judgment of an Endoscopy Technician — deciding whether to release or quarantine a scope after a failed leak test or minimum-effective-concentration (MEC) reading, handling a point-of-use treatment delay against the manufacturer's reprocessing window, triaging a borescope finding on a working channel, or troubleshooting equipment and passing accessories during a live GI procedure.
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 task needs the judgment of a Control and Valve Installer/Repairer — sizing a control valve's Cv against actual process flow, diagnosing a limit-cycling control loop as valve stiction versus a bad PID tune, running an FCI 70-2 seat-leakage or API 598 pressure test, calibrating a positioner/actuator after a rebuild, or scheduling PHMSA-mandated regulator/relief-valve testing on a gas distribution system.
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 task needs the judgment of a Chemical Plant and System Operator — reconciling a batch's mass balance to catch a leak or unaccounted reaction that individually normal readings would miss, responding to an early exotherm temperature trend before it accelerates, prioritizing simultaneous alarms by consequence severity rather than order of occurrence, or flagging a combination of near-limit variables that no single-variable check would catch.
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 task needs the judgment of an auto-glass installer/repairer — deciding whether a chip or crack qualifies for resin repair versus full replacement, calculating a safe drive-away time for the adhesive and conditions on a job, determining whether a windshield replacement triggers ADAS camera recalibration, or diagnosing a cut-out/pinch-weld defect before it becomes a leak or corrosion claim.
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 a monthly money check-in with an AI assistant: categorize spending, spot leaks, set one savings target, and view it through the 50/30/20 lens — judgment-free. Use once a month to stay aware of where your money goes. General educational information, not financial advice.
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。
Audit codebases for leaked secrets and hardcoded credentials, generate .env templates, configure secrets management with AWS Secrets Manager, Vault, Doppler, or GCP Secret Manager, set up credential rotation, and integrate secrets into CI/CD pipelines via OIDC federation
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。
Profile and audit mobile app performance -- cold/warm/hot startup time, memory leaks, battery drain, network efficiency, frame rate jank, and binary size. Triggers: diagnosing slow launches, investigating ANRs, reducing app size, or benchmarking rendering 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。
Build QA quality dashboards, calculate defect metrics, analyze escape rates, and produce quality trend reports. Use this skill whenever a QA lead or manager needs to measure and communicate quality outcomes. Trigger on phrases like "quality metrics", "defect density", "escape rate", "bug trends", "quality dashboard", "sprint quality report", "test coverage metrics", "QA KPIs", "how is quality trending", "defect leakage", "production bugs this quarter", "mean time to detect", "quality scorecard", or any request to quantify, visualize, or report on the quality of a product or testing process. Also trigger when a manager asks "how is QA performing?" or an engineering lead asks "what does our defect data tell us?".
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。
Redact PII in your own code before it reaches Agentforce prompts, models, and logs. Trigger keywords: agentforce pii, pii redaction, data masking llm, prompt pii filter, audit pii leakage. NOT for turning on the platform's own masking, zero-retention and audit-trail controls — use agentforce/einstein-trust-layer. NOT for masking PII in a refreshed sandbox — use security/sandbox-data-masking.
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。
Quicken the codebase — an ad-hoc performance audit that reveals what drags (N+1 queries, needless re-renders, missing caching, memory leaks, redundant computation, hot-path complexity) and prescribes the fix, surfaced inline in the session. Scoped to your current diff by default, or to a path/area you name. Use before a perf-sensitive merge, when something feels slow, or when you want a targeted optimization pass. Replaces the old always-on `optimize` daemon worker (alias: /perf-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。