테스트·보안 스킬

Load when creating, auditing, refactoring, or repairing an Agent Skill for reuse, governance, and runtime efficiency.

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Build sample/demo screens that make library options explorable. Add controls, previews, presets, sample data, and QA surfaces in the app module.

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Establish, author, restructure, or audit human-first processor Architecture, Design, Research, Review, and Verification documentation. Use for progressive documentation scaffolding, authority maps, reading paths, document-type content contracts, length-budgeted splitting, or maintainability reviews. Do not use as a substitute for cycle-accurate microarchitecture analysis or RTL implementation.

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Task-sized forensic analysis of Vivado synthesis and routed timing evidence for processor RTL. Use when Codex must trace a named setup or hold path, audit whole-design timing populations, compare implementation runs, map primitive, LUT, CARRY, BRAM, DSP, MUXF, register, and routed-net stages back to Chisel or generated RTL, distinguish logic from routing pressure, identify missing DCP queries, or write an evidence-backed timing report. This skill is read-only by default and stops at ranked modification directions. Use optimize-chisel-fpga-timing for RTL edits and routed A/B implementation closure.

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Search for raw byte patterns (hex sequences, opcodes) in binary code. Use when looking for specific instruction sequences, machine code patterns, UEFI SMI handlers, or known vulnerability signatures by their byte representation.

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Track data flow between function parameters, calls, and arguments using taint analysis. Use when detecting vulnerabilities like command injection, buffer overflows, or tracing user input to dangerous functions.

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Safely localize unknown Unity games, especially Japanese-to-Chinese projects. Use to discover Unity version/layout/backend, migrate translations across game versions, extract occurrence-mapped text from serialized assets, embedded CSV, bytecode, UI, Localization StringTables, Utage, or middleware, bridge rows through AiNiee-style translation, validate and transactionally write approved candidates, audit residual and runtime-only text or AutoTranslator diffs, diagnose TMP fonts and Addressables/IL2CPP failures, locate saves, or acceptance-test SHA-verified patches.

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Translate books, documents, AiNiee projects, Unity localization caches, or flat MTool JSON dictionaries end to end on the coding agent itself without the AiNiee app. Use for requests such as "用 agent 翻译这本书", "把 epub 翻译了", "翻译 Unity CSV/cache", "把这个 MTool JSON 翻译了", or "AiNiee-style translation" that need a locked glossary, resumable batches or bounded subagent groups, terminology consistency, source-aware QA, validated cache writeback, and export. For MTool, preserve the left JSON key as immutable source text and write only the right value as translation.

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Comprehensive regression model diagnostics and assumption checking. Use when validating regression models, checking assumptions (linearity, homoskedasticity, normality, independence), detecting outliers/influence, testing multicollinearity, or when user mentions residuals, heteroskedasticity, VIF, Cook's distance, or diagnostic plots.

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Handle ethical requirements, IRB approval, trial registration, and pre-analysis plans for RCTs. Use when user mentions: research ethics, IRB, informed consent, trial registration, pre-analysis plan, PAP, data security, vulnerable populations, research transparency.

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Create new skills, modify and improve existing skills, and measure skill quality. Use when users want to create a skill from scratch, edit or refine an existing skill, test a skill with sample prompts, or optimize a skill's description for better triggering accuracy.

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One-shot GEO visibility check for a brand using Xpoz. Runs the buyer questions that matter through Claude, ChatGPT, and Gemini, and reports where the brand appears, who wins instead, and which surfaces the answers are assembled from. Use when asked to "check my AI visibility", "does ChatGPT recommend us", "GEO audit", "where do we show up in AI answers", or "who wins our category in AI answers".

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Monitor social platforms for security threats, vulnerability discussions, and breach intelligence using Xpoz. Use when asked to "find CVE discussions", "security threat monitoring", "OSINT social media", "vulnerability intelligence", "breach mentions", or "threat intel from Twitter/Reddit".

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Use when authoring Litestar skill content, Python/TypeScript examples, PEP 604, async I/O, Google docstrings, ruff/mypy/pyright, pytest, or CI rules. Not for focused Litestar APIs.

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Auto-activate for litestar_security, SecurityPlugin, SecurityConfig, SecureController, CurrentUser, Principal, SecurityContext, or requires_role/scope/tenant/capability/assurance. Not for basic Litestar Guards.

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Auto-activate for pytest_databases, Docker DB fixtures, PostgreSQL/pgvector/ParadeDB, MySQL/MariaDB, Oracle/SQL Server, CockroachDB, MongoDB, Redis/Valkey, BigQuery/Spanner, MinIO, or RustFS. Not for mocked DBs.

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Auto-activate for test_*.py, conftest.py, litestar.testing, TestClient, AsyncTestClient, create_test_client, create_async_test_client, anyio, Guard mocks, or DI overrides. Not for generic pytest.

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Auto-activate for polyfactory, ModelFactory, DataclassFactory, MsgspecFactory, AttrsFactory, Use, register_fixture, pytest plugin, __random_seed__, or coverage(). Not for production seeding.

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Perform comprehensive technical reviews of Ruby on Rails applications. Runs automated analysis tools (RubyCritic, Brakeman, bundler-audit, Gitleaks, Debride, linters, SimpleCov, Rails stats, Rails ERD), analyzes code for architecture, security, authorization (Pundit/CanCanCan), dead code, and design issues, and produces a structured markdown report with prioritized findings and a 0-10 score. Use when the user requests a tech review, code audit, project assessment, or quality analysis of a Rails application.

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Claude Code 專案配置審計。觸發：review/優化 CLAUDE.md、skills、settings、定期清洗累積內容、新專案上線前檢查。

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AI Agent 安全設計模式 — 基於 Claude Code 七層縱深防禦架構的逆向分析。Use when: 設計工具安全機制、實作命令過濾、建構權限模型、設計 sandbox。

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Prove — or honestly fail to prove — a specific technical claim: "the fix works", "it's deployed", "the service is running", "the site is done", "there are no vulnerabilities". Atomizes the claim, picks the shortest decisive signal on the evidence ladder, and returns a verdict from a fixed vocabulary (pass / partial / fail / skip) with an explicit coverage boundary. NOT code review, NOT a fix, NOT orchestration — only "what is proven, and what we merely hope". Triggers — "prove it works", "is it really done?", "are you sure?", "we deployed — is it live?", "verify that", "did we actually check?".

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Evaluate an incoming workflow upgrade — a skill, MCP server, subagent, library, external service, prompt, or pattern — through one disciplined pipeline before adopting it. Security is a blocking gate. Records every verdict so the same thing is never evaluated twice. Use when considering a new tool from a post, repo, or recommendation. Triggers — "vet this", "should we adopt", "is this safe to install", "evaluate this skill/MCP", "what do you think of this tool".

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Autonomous goal loop — give it ONE goal plus how to verify it, the agent runs check→fix→check itself and comes back only with the finished result (code with audit, research with fact-check, content with critique, audit/backtest). Self-verification instead of constant supervision, with hard safety guards. Vary the loop with a verification rubric designed before it runs. Triggers — "work autonomously to the result", "don't come back until it's done", "run the loop yourself", "drive it to done".

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