1,125 件 ・ 関連度順
概要と使いどころ
Optimize cloud and AI costs for modern SaaS stacks (Vercel, Supabase, Neon, Stripe, and AI APIs). Covers token efficiency, serverless database scaling, edge function optimization, and burn rate monitoring. Use when planning infrastructure, investigating high bills, or auditing API usage.
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。
Generative Engine Optimization plus agent readiness. Use this skill whenever the user asks about GEO, AEO, LLMO, optimizing content for AI search engines (ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini, Copilot), getting cited by LLMs, brand mentions in AI responses, llms.txt, robots.txt for AI crawlers, Schema.org JSON-LD, FAST framework, agent readiness, MCP server discovery, Web Bot Auth, OAuth for agents, API Catalog, agentic commerce (x402, ACP, UCP), Markdown content negotiation, or auditing a site or page for AI visibility. Also trigger when the user says "AI SEO", asks how to "rank in ChatGPT", "show up in Perplexity", "appear in AI Overviews", or wants a site to "speak to AI agents". Trigger even when the request is implicit, such as "review this article so AI search picks it up" or "make our docs agent-friendly".
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 content against your brand voice, style guide, and messaging pillars, flagging deviations by severity with specific before/after fixes. Use when checking a draft before it ships, when auditing copy for voice consistency and terminology, or when screening for unsubstantiated claims, missing disclaimers, and other legal flags.
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。
Forensic scan for npm/PyPI supply-chain compromise (Shai-Hulud/TeamPCP campaigns) — checks persistence, hook injection, compromised packages, IOC strings, C2, dead-man switches. Use when auditing a machine or repo for malicious dependencies. Trigger on "supply chain audit", "check for compromised packages", "Shai-Hulud", "scan for malicious deps".
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。
Detect technical debt — duplicated code, dead code, TODOs, and oversized functions across the codebase. Use at the end of a session or when auditing code hygiene. Trigger on "find tech debt", "any dead code", "duplicated code", "code hygiene", "techdebt".
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 planning, drafting, revising, compressing, or auditing conference rebuttals and author responses in OpenReview Markdown or PDF. Supports exact reviewer mapping, evidence and experiment integration, guided risk decisions, venue-format validation, and blind reviewer/AC audits.
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 REST and GraphQL API designs for consistency, usability, and best practices. Covers naming conventions, versioning strategy, error format, pagination, authentication patterns, and breaking change detection. Use when reviewing API specs, designing new APIs, or auditing existing endpoints.
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。
Read and interpret financial statements — Income Statement, Balance Sheet, and Cash Flow Statement. Calculate key ratios, identify trends, and produce an analysis narrative. Use when reviewing company performance, preparing for investor meetings, or conducting due diligence. Do not use for preparing or auditing financial statements — this is an analysis tool 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。
Smart contract vulnerability playbook. Use when auditing Solidity/EVM contracts for reentrancy, integer overflow, access control, delegatecall, flash loan, signature replay, and MEV-related attack 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。
Use when auditing GitHub Actions workflows for script injection vulnerabilities via unsanitized context expressions. Trigger on: "github actions injection", "workflow injection", "head_ref injection", "github context injection", "pwn request", "github.head_ref", "github.event.pull_request.title", "github.event.issue.body", pull_request_target workflows, run: steps interpolating GitHub context variables, CI/CD script injection, GitHub Actions security 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。
DOM-based XSS occurs when JavaScript reads attacker-controlled sources (`location.hash`, `document.referrer`, `window.name`, `location.search`) and passes them to dangerous sinks (`document.write`, `innerHTML`, `eval`, `location.href`, `setTimeout`, `jQuery.html()`) without sanitization. Unlike reflected/stored XSS, payloads never reach the server. Detect by auditing JavaScript for tainted data flow from DOM sources to sinks. Tools: Burp Suite DOM Invader, Chrome DevTools, DOMPurify (fix).
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。
Identify and exploit Cross-Origin Resource Sharing (CORS) misconfigurations. Use this skill when auditing APIs or web applications that share sensitive data across domains, forcing victims' browsers to inadvertently leak private information (e.g., API keys, PII, CSRF tokens) to an attacker-controlled website.
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。
General workflow for auditing ML CI failures, experiment regressions, training run failures, golden metric failures, and telemetry-backed ML work-product claims from local repositories, logs, metrics, configs, and artifacts. Use when Codex needs to decide whether an ML failure is a model/convergence issue, correctness bug, data/config issue, infrastructure/runtime issue, evaluation/gating policy issue, or unsupported claim, and produce structured evidence-backed outputs.
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 and populate tracked stablecoin compliance sidecars for the U.S. GENIUS Act, EU MiCA, or both. Use when adding or auditing authorization, applicability, pathway, or disclosure evidence.
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。
Curate evidence-backed collateralQuality and whole-book custodyModel labels. Use when adding a stablecoin or auditing Selector custody eligibility and DDR depeg-duration verdicts.
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。
Top-level orchestrator for the Gaia Skill Tree evidence verification pipeline. Run this when you want to do a full evidence pass — collecting raw sources, verifying live GitHub star counts, running Phase 2B benchmark-source verification, adversarially auditing for quality/formatting issues, and checking that every URL is reachable — all in one coordinated sequence. Trigger phrases: "run the evidence pipeline", "full evidence pass", "verify evidence", "evidence verification pipeline", "run ev-pipeline", "check all evidence", "audit the data lake", "evidence quality sweep", "refresh evidence", "validate evidence sources". Also aliased as /evidence-verification-pipeline. Use the individual sub-skills (ev-collection, ev-star-verification, ev-adversarial-audit, ev-link-validation) only when you need to re-run one phase in isolation; for end-to-end work, always start here.
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。
Canonical entry point for the full Gaia evidence verification pipeline. Invoke as /evidence-verification-pipeline or /ev-pipeline — both names trigger this skill. Use whenever you need to run the evidence phases end-to-end: collecting raw evidence from the data lake, verifying live GitHub star counts, running Phase 2B benchmark-source verification, running adversarial auditing for noise and URL errors, and checking link health via Firecrawl. Also use when someone says "run the evidence pipeline", "verify the evidence lake", "audit evidence", "run ev-pipeline", "full evidence check", "pipeline run", "prepare evidence for ingestion", or "refresh the data lake". This is the pre-ingestion step — run it before importing any evidence into the registry.
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。
Focused single-target audit of one Gaia skill, named skill, or real-skill catalog item. Use this skill when: auditing a specific skill by ID or name, a skill looks overpromoted, evidence feels thin or dead, a contributor's skill seems outdated or superseded, the rank doesn't match the source quality, evidence is mapped to the wrong Evidence Type, a skill at 3★+ is missing a working GitHub link, someone asks "does this skill deserve its stars?", or you want the smallest possible correction with validation — not a bulk sweep. This is NOT for bulk curation passes (use /gaia-curate for that) or meta-level registry sweeps (use /gaia-meta-audit). It is specifically for "fix this one skill, get it right."
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 the user wants to be taught an Anthropic product or feature - "teach me Claude Code", "learn Cowork", "academy lesson", "train me on MCP or the Claude API", "quiz me", "next lesson" - or asks for structured learning rather than a one-off answer. Covers Claude Code, Claude.ai, Cowork, Tag, Platform/API, and AI Fluency. Do NOT use for auditing how the user already works (use academy-review) or for mid-task help where the user just wants their task done.
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。
Reverses Android malware: unpacking APKs, decompiling DEX bytecode to readable Java, auditing the manifest for abused permissions and components, and locating dynamically loaded or native payloads. Activates for requests to analyze an APK, decompile DEX, or investigate a suspicious Android app.
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。
Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that same input. Use when: (1) designing a classifier/LLM extractor whose target is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. Symptom of the underlying failure: elaborate theory accumulates to explain why gold is "partially unpredictable" when the model was simply never shown the document the annotators read.
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。
Security-first code review & vulnerability prevention (IDOR, XSS, CSRF, SSRF, SQLi, Auth/JWT, Mass Assignment, Sensitive Data Exposure). Use when writing web features, designing endpoints, or auditing security.
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 cytotechnologist — screening a gynecologic (Pap test) slide and applying Bethesda System criteria, assessing non-gynecologic specimen adequacy (FNA rapid on-site evaluation, body fluid, brushing) against the relevant reporting system, deciding whether a finding can be signed out independently or must be referred to a pathologist, or auditing screening quality metrics (ASC:SIL ratio, rescreen concordance, workload compliance). A reasoning aid, not a substitute for a certified cytotechnologist or pathologist signing out an actual case.
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 Document Management Specialist — designing or auditing a records retention schedule, planning a content migration between DMS platforms (SharePoint, OpenText, Documentum, M-Files), reconciling migrated vs. source document counts, resolving a version-control or legal-hold conflict, or scoping a defensible-disposal (ROT) 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。