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Use when the user wants a research packet plus a decision-complete implementation plan for a product, feature, algorithm, prompt, bugfix, or refactor, but does not need the full explicit phase-by-phase build loop narration.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Operational lessons for maintaining a Claude Code plugin marketplace. Load whenever making changes to `.claude-plugin/marketplace.json`, bumping a plugin version that's listed in a marketplace, adding/removing plugins from a marketplace, or troubleshooting "add marketplace" failures. Prevents schema rejection, stale manifests, CDN-lag false negatives, and install-command drift.
How to publish a public npm package to GitHub Packages for any project under the @tyroneross scope — Claude Code plugins, build-loop subsystems, prompt-builder, etc. Load whenever the user asks to "publish to GitHub Packages", "publish the plugin", "ship as an npm package", "make this installable", "release this to npm", or whenever bumping a package version that needs to ship. Covers single-package repos (build-loop pattern) and monorepo per-plugin publishing (RossLabs-AI-Toolkit pattern). Includes the GitHub Actions workflow template, the release ritual, and the verification checklist that catches the 403 / version-drift / scope-missing failures already seen in this account.
Use when building an evaluation harness for an LLM agent, RAG pipeline, or prompt — when the question is "is this actually working?" Encodes Anthropic and OpenAI's published 2025–2026 eval guidance — start with 20–50 real-failure tasks (not synthetic happy-paths), use three grader types in the right places (deterministic code / LLM judge / human review), measure pass@k vs pass^k for production reliability, grade outcomes not reasoning paths, calibrate the LLM judge against human review before trusting it, and detect eval saturation. Triggers on "build an eval", "build evals", "eval harness", "is this prompt working", "regression test for LLM", "how do I know my agent is better", "LLM judge", "pass@k", "compare two prompts on real data", "the prompt looks fine but production is broken". Vendor-neutral with starter templates.
Use when designing the surface of a tool that an LLM agent will call — function declarations, MCP server methods, OpenAI function-calling schemas, Anthropic tool_use definitions, or Agent SDK tools. Encodes Anthropic and OpenAI's published 2025–2026 guidance — namespaced names (service_resource_verb), semantically specific parameters (user_id over user), signal-dense return formats with a response_format knob, prompt-engineered error strings that guide the model toward the fix, and the anti-pattern of wrapping every API endpoint 1:1. Triggers on "design a tool for an agent", "write a function declaration", "MCP server tool", "function calling schema", "tool_use definition", "Agents SDK tool", "the agent keeps calling my tool wrong", "the agent loops on a tool error", "list-all is blowing up context", "tool descriptions". Cross-vendor; vendor wire-format notes in references/.
Use when building or operating a coding agent that has to work across multiple context windows or multiple sessions — when one shot won't fit. Encodes Anthropic's published 2025 harness pattern — initializer/coding split, claude-progress.txt + feature-list JSON + git commits as cross-context state, mandatory session-init protocol (read logs → review features → run e2e → start dev server), edit-status-only contract on the feature file. Triggers on "long-running agent", "multi-session agent", "context window keeps filling up", "agent forgets between sessions", "session handoff", "Codex to Claude Code handoff", "Claude Code to Codex handoff", "harness for an agent", "what does the agent read on session start". Bridges bookmark (session snapshots) and build-loop (single-session orchestration).
Use when designing a prompt or agent that will run at scale and you want to maximize prompt-cache hit rate. Encodes the cross-vendor invariant — order content static-to-dynamic so the cacheable prefix is stable — plus Anthropic's explicit cache_control breakpoint mechanics (4-breakpoint budget, 20-block lookback, 5m vs 1h TTL) and OpenAI's automatic prefix hashing (≥1024 token threshold, 128-token increments, 5–10min idle TTL, up to 24h on gpt-5.5+). Triggers on "prompt caching", "cache hit rate", "reduce LLM cost", "ephemeral cache", "cache_control", "prompt is slow on every call", "we're paying for the same tokens twice", "TTL", "cache breakpoint". Vendor-neutral spine plus per-vendor branches in references/.
| name | research-build |
| description | Use when the user wants a research packet plus a decision-complete implementation plan for a product, feature, algorithm, prompt, bugfix, or refactor, but does not need the full explicit phase-by-phase build loop narration. |
Load the build-loop-auto-research skill when the work is multi-step, integration-heavy, or likely to need iteration.
Use this skill as the shorter packet-oriented path:
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/research_build.py --task "{{user_request}}" --repo-path "$PWD" --format markdown
high, medium, or low.medium and low. Spot-check high, then re-calibrate.Return the final answer in this order:
Bottom lineWhat I foundBest pathWhy this pathRisks / unknownsNext action