bundled/skills/deepagent-toolchain-plan/SKILL.md
DeepAgent-style tool discovery for VCO: propose a minimal skill/tool chain (with verification points) and reduce confirm_required friction.
npx skillsauth add foryourhealth111-pixel/vco-skills-codex deepagent-toolchain-planInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when:
route_mode=confirm_required and you want a better, evidence-backed choiceDeepAgent upstream is vendored for reference / optional advanced runs:
C:\Users\羽裳\.codex\_external\ruc-nlpir\DeepAgent\VCO-managed runtime config and self-check scripts (no secrets stored/printed):
C:\Users\羽裳\.codex\skills\vibe\config\ruc-nlpir-runtime.jsonpwsh C:\Users\羽裳\.codex\skills\vibe\scripts\ruc-nlpir\preflight.ps1Return a toolchain with:
Run the router script in probe mode to get candidates + overlays in a machine-readable form:
pwsh C:\Users\羽裳\.codex\skills\vibe\scripts\router\resolve-pack-route.ps1 -Prompt "<PROMPT>" -Grade L -TaskType planning -Probe -ProbeLabel "toolchain" -ProbeOutputDir outputs/runtime/router-probesThen use the emitted confirm_ui + overlay advice to decide the chain.
Prefer a chain that:
web.run (fast structured browse)playwright / turix-cua (dynamic/interactive)webthinker-deep-research (Lite) → outputs/webthinker/.../report.mdflashrag-evidence (local protocol checks) → citeable snippetscode-reviewer (if code changes) or verification-quality-assurance (if routing changes)flashrag-evidence (locate existing policy/overlays)writing-plans (implementation plan with file paths + verify steps)verification-before-completion (run check + router probe)development
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
development
Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.
development
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
development
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.