테스트·보안 스킬

Web search using curl + multiple search engines (Baidu, Google, Bing, DuckDuckGo). Activates when user asks to search, look up, or query something online. Includes security enhancements: input sanitization, command injection protection, and URL encoding.

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Remove signs of AI-generated writing from prose, and keep it out of a repo. Use when drafting, editing, or reviewing text to make it sound natural and human, or when auditing a whole docs folder for AI slop. Detects 41 patterns across content, language, style, communication, filler, and rhetoric, including: significance inflation, promotional language, -ing tails, vague attributions, AI vocabulary, copula avoidance, negative parallelisms, false agency, em dash overuse, chatbot artifacts, hedging stacks, staccato drama, and aphorism formulas. Includes voice calibration, a no-fabrication rule, statistical tells, and a draft -> audit -> final rewrite loop.

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Write TEST-STRATEGY.md, a test plan per RFC, before the tests are written.

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Review an implemented RFC in a fresh context against its acceptance criteria, RULES.md and the test plan, and save the review to reviews/.

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Use when writing, testing, or preparing ANY contribution or pull request to the JSONbored/awesome-claude (HeyClaude) repo — adding a community content entry (agent/MCP server/skill/hook/command/rule/guide/collection/statusline), or making a platform/code change (website, registry package, MCP package, scripts). The repo has two separate review paths: a private, one-shot "HeyClaude submission gate" that decides single-file content PRs, and the shared gittensory-orb bot that posts an advisory readiness/linked-issue comment on platform/code PRs but does not itself merge or close content PRs. Unlike JSONbored/gittensory and JSONbored/metagraphed, a linked issue is OPTIONAL here — do not import their mandatory-linked-issue rule into this repo. Invoke for any "contribute to / open a PR against / submit an entry to / fix a bug in awesome-claude (HeyClaude)" task.

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Maintenance of the contributor issue pipeline for JSONbored/awesome-claude (HeyClaude) — closing issues that are already done but not marked so, and keeping a small, high-value contributor-available backlog stocked with real website/content/feature/bugfix work. Runs every ~24h via a dedicated scheduled task. Invoke for "run the issue gardening", "audit open issues for stale/complete ones", "generate new contributor issues", or any recurring/scheduled run of this process. `reference.md` (next to this file) has the exhaustive label/milestone/ template detail — read it before doing real work, not just this file. This is the awesome-claude-specific instance; JSONbored/loopover and JSONbored/metagraphed each have their own separate copy with different conventions and a much larger backlog target — do not cross-apply either repo's specifics to this one without being asked.

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Use when scanning code for security vulnerabilities. Use when user says "scan security", "kiểm tra bảo mật", "security audit", "review security", or invokes `/vbs-scan-security`. For large scans (>20 main-language files OR >30 total OR >14 days) processes chunks sequentially. Outputs bilingual reports (vi/en). Optional `--auto-fix` (agentic patch + verify loop) and `--sca` (live CVE lookup via OSV.dev).

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Use when scanning code for security vulnerabilities. Use when user says "scan security", "kiểm tra bảo mật", "security audit", "review security", or invokes `/vbs-scan-security`. Auto-delegates to sub-agents for large scans (>20 main-language files OR >30 total OR >14 days). Outputs bilingual reports (vi/en). Optional `--auto-fix` (agentic patch + verify loop) and `--sca` (live CVE lookup via OSV.dev).

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Verify an open factory PR before human review - re-runs gates, checks the test proves the fix, checks scope and test tampering. Use when reviewing a factory PR, running the PR review routine, or asked to independently check a change.

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Independently verify a factory pull request, including fail-closed gates, negative test proof, scope, and load-bearing review.

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Classify GitHub issues into the factory's live label queue and write an auditable snapshot. Use for backlog triage or a scheduled triage run.

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Inspect factory health, stale work, CI, security advisories, and recent run evidence without implementing fixes.

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Classify incoming issues into the live GitHub label queue, reproduce where cheap, and write an auditable QUEUE.md snapshot. Use when triaging a backlog, running the scheduled triage routine, or deciding what the factory should pick up next.

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Scheduled health sweep that closes the factory loop - reads CI failures, recent commits, dependency and security advisories, and live queue labels, then files issues and writes an immutable run record. Use for the nightly or weekly monitor routine.

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Read-only checkup of installed agent skills across Claude Code and Codex: broken links or files, same-name copies that drifted apart, descriptions that compete or miss requests their skill already handles, and skills loaded by both agents but used by only one. Use when the user asks to check, audit or tidy their skills, compare Claude Code and Codex skills, asks why a skill did not trigger, or wants to test a reworded description against past requests. For unused Claude Code skills and token cost alone, Claude Code's /skill-doctor is enough. Not for agent config health or raw usage rankings (skill-usage-stats) or policy-driven ownership, exposure and retirement repairs (skill-ecosystem-doctor).

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Statistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.

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Mandatory audit workflow for UI/UX changes that reads current state FIRST, checks for redundancy, respects clean design philosophy, and identifies genuine gaps before implementation. Auto-invoked when user mentions UI, UX, design, layout, homepage, page improvements, visual changes, or interface modifications.

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Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.

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Use to stress-test predictions by assuming they failed and working backward to identify why. Invoke when confidence is high (>80% or <20%), need to identify tail risks and unknown unknowns, or want to widen overconfident intervals. Use when user mentions premortem, backcasting, what could go wrong, stress test, or black swans.

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Use when validating product assumptions before building, discovering unmet user needs, understanding customer problems and workflows, testing concepts or positioning, researching target markets, identifying jobs-to-be-done and hiring triggers, uncovering pain points and workarounds, or when users mention user research, customer interviews, surveys, discovery interviews, validation studies, or voice of customer.

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Investigate how a concept, code path, application, network flow, system, incident, document, or local artifact actually works, then deliver a two-depth visual HTML explainer with a dead-simple big-picture first layer and source-backed technical depth behind it. Use when the user invokes $xray, asks how something works under the hood, wants a module or app behavior traced across boundaries, requests web, code, network, or safe static artifact research before explanation, or asks for an evidence-backed explainer. Do not use for answer-only definitions, general web design, full codebase audits, or invasive binary reverse engineering without explicit authorization.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Use when a skill is brittle, drifting, repeatedly failing, or needs a Reliable Skill Contract. Trigger for phrases like skill lifeguard, reliable skill, self-maintaining skill, negative examples, verification checkpoints, drift signals, replay hooks, or failure log to skill patch. Audits or patches skills so high-value workflows include explicit forbidden behaviors, checkpoints, machine-checkable done conditions, replay or smoke hooks, and drift detection.

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Route substantial coding or repository-review work through a two-stage Sol commander/verifier and separate Luna Max CLI worker. Use when the user explicitly asks Sol to direct or supervise Luna, explicitly requests this router, needs an isolated auditable Luna-owned implementation with a Sol review loop, or asks to diagnose or measure the router itself. Do not use merely because a task involves coding, verification, progress reporting, or token saving. For small or straightforward tasks, keep work in Sol unless the user explicitly requests Luna; router configuration, usage, and efficiency questions should use read-only inspection without launching a worker.

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