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senior-ml-engineer
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.
원본 환경 확인 필요
senior-prompt-engineer
Use when the user asks to optimize prompts, design prompt templates, evaluate LLM outputs with an eval set, measure RAG retrieval quality, validate agent/tool configurations, analyze token usage, or design structured-output contracts. Covers eval-driven prompt iteration, RAG metrics (relevance, faithfulness, coverage), agent workflow validation, and token/cost budgeting — all model-agnostic, with three stdlib Python tools.
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senior-qa
Generates unit tests, integration tests, and E2E tests for React/Next.js applications. Scans components to create Jest + React Testing Library test stubs, analyzes Istanbul/LCOV coverage reports to surface gaps, scaffolds Playwright test files from Next.js routes, mocks API calls with MSW, creates test fixtures, and configures test runners. Use when the user asks to "generate tests", "write unit tests", "analyze test coverage", "scaffold E2E tests", "set up Playwright", "configure Jest", "implement testing patterns", or "improve test quality".
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senior-secops
Senior SecOps engineer skill for application security, vulnerability management, compliance verification, and secure development practices. Runs SAST/DAST scans, generates CVE remediation plans, checks dependency vulnerabilities, creates security policies, enforces secure coding patterns, and automates compliance checks against SOC2, PCI-DSS, HIPAA, and GDPR. Use when conducting a security review or audit, responding to a CVE or security incident, hardening infrastructure, implementing authentication or secrets management, running penetration test prep, checking OWASP Top 10 exposure, or enforcing security controls in CI/CD pipelines.
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senior-security
Use when the user asks for STRIDE threat modeling, DREAD risk scoring, data-flow-diagram threat analysis, or a quick secret scan — or when a security request needs routing to the right specialist skill (pen-testing, incident response, cloud posture, red team, AI security, threat hunting, secure code review). This skill owns threat modeling; everything else routes to a sibling.
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stripe-integration-expert
Production-grade Stripe integrations: subscriptions with trials and proration, one-time payments, usage-based billing, checkout sessions, idempotent webhook handlers, customer portal, and invoicing. Covers Next.js, Express, and Django patterns. Use when integrating Stripe for the first time, debugging webhook reliability issues, migrating from a different payment provider, or adding usage-based billing to an existing subscription product.
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tdd-guide
Test-driven development skill for writing unit tests, generating test fixtures and mocks, analyzing coverage gaps, and guiding red-green-refactor workflows across Jest, Pytest, JUnit, Vitest, and Mocha. Use when the user asks to write tests, improve test coverage, practice TDD, generate mocks or stubs, or mentions testing frameworks like Jest, pytest, or JUnit.
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tech-stack-evaluator
Technology stack evaluation and comparison with TCO analysis, security assessment, and ecosystem health scoring. Use when comparing frameworks, evaluating technology stacks, calculating total cost of ownership, assessing migration paths, or analyzing ecosystem viability.
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threat-detection
Use when hunting for threats in an environment, analyzing IOCs, or detecting behavioral anomalies in telemetry. Covers hypothesis-driven threat hunting, IOC sweep generation, z-score anomaly detection, and MITRE ATT&CK-mapped signal prioritization.
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snowflake-development
Use when writing Snowflake SQL, building data pipelines with Dynamic Tables or Streams/Tasks, using Cortex AI functions, creating Cortex Agents, writing Snowpark Python, configuring dbt for Snowflake, or troubleshooting Snowflake errors.
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agent-harness
Turn any domain folder of skills into a bounded agentic loop: compile a goal into a verifiable task plan, execute tasks with the domain's own tools, verify every task with machine-run checks, retry with caps, escalate to a human when budgets exhaust, and refuse to close until everything is verified or explicitly waived. Use when you want an agent or subagent to pick up a goal and drive it to a verified close across one of this repo's 18 domains ('run this goal through the engineering harness', 'set up an agentic loop for marketing work', 'make the finance domain self-verifying'). NOT for authoring Claude Code Workflow-tool .js scripts (workfl
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agent-memory
Use when a project's CLAUDE.md has grown past what anyone reads and you want the agent to learn durable facts from its own sessions instead — or when asking why the agent keeps re-learning the same correction, why a remembered rule is wrong, or where a memory line came from. Implements a four-tier store (L0 transcripts / L1 candidates / L2 project context / L3 stable persona) where promotion is earned by recurrence across sessions and days, never by one confident statement, and nothing reaches a committed file without a human adopting it.
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agenthub
Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
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board
Read, write, and browse the AgentHub message board for agent coordination. Use when the user runs /hub:board or asks to post, read, or inspect coordination messages between competing AgentHub agents.
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eval
Evaluate and rank agent results by metric or LLM judge for an AgentHub session. Use when the user runs /hub:eval or asks to score, compare, or pick a winner among completed AgentHub agents.
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hub-init
Create a new AgentHub collaboration session with task, agent count, and evaluation criteria. Use when the user runs /hub:hub-init or asks to start a multi-agent competition on a task.
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hub-status
Show DAG state, agent progress, and branch status for an AgentHub session. Use when the user runs /hub:hub-status or asks how the AgentHub agents are doing.
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merge
Merge the winning agent's branch into base, archive losers, and clean up worktrees. Use when the user runs /hub:merge or asks to land the winning AgentHub result and tidy the session.
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run
One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation. Use when the user runs /hub:run or asks to execute a full AgentHub competition end-to-end.
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spawn
Launch N parallel subagents in isolated git worktrees to compete on the session task. Use when the user runs /hub:spawn or asks to start the competing agents for an initialized AgentHub session.
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ar-resume
Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating. Use when the user runs /ar:ar-resume or asks to pick up a previously started autoresearch experiment.
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ar-status
Show experiment dashboard with results, active loops, and progress. Use when the user runs /ar:ar-status or asks how an autoresearch experiment is going.
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autoresearch-agent
Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.
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loop
Start an autonomous experiment loop with user-selected interval (10min, 1h, daily, weekly, monthly). Uses CronCreate for scheduling. Use when the user runs /ar:loop or asks to run an autoresearch experiment continuously on a schedule.
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run
Run a single experiment iteration. Edit the target file, evaluate, keep or discard. Use when the user runs /ar:run or asks for one manual autoresearch iteration.
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setup
Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator. Use when the user runs /ar:setup or asks to start optimizing a file with the autoresearch loop.
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behuman
Use when the user wants more human-like AI responses — less robotic, less listy, more authentic. Triggers: 'behuman', 'be real', 'like a human', 'more human', 'less AI', 'talk like a person', 'mirror mode', 'stop being so AI', or when conversations are emotionally charged (grief, job loss, relationship advice, fear). NOT for technical questions, code generation, or factual lookups.
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book-to-skill
Converts books, documentation folders, and source collections (PDF, EPUB, DOCX, HTML, Markdown, RST, AsciiDoc, RTF, MOBI/AZW) into structured agent skills — extracting named frameworks, principles, techniques, and anti-patterns into a master SKILL.md plus on-demand chapter files, a glossary, a patterns file, and a decision cheatsheet. Use when the user wants to study a document with an agent, apply an author's frameworks while working, turn internal docs or standards into a reusable knowledge base, or package a compiled book skill as a claude-skills plugin.
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boost-asio-pro
Use when writing or reviewing asynchronous C++ networking code with Boost.Asio or standalone Asio — TCP/UDP servers and clients, SSL/TLS, timers, strands, io_context, co_spawn, awaitable, async_read/async_write, asio::spawn, yield_context, or pre-C++20 completion-handler callbacks.
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caveman
> Ultra-compressed communication mode. Cuts token usage ~75% by dropping filler, articles, and pleasantries while keeping full technical accuracy. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman.
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