deepagent-toolchain-plan
DeepAgent-style tool discovery for VCO: propose a minimal skill/tool chain (with verification points) and reduce confirm_required friction.
Best use case
deepagent-toolchain-plan is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
DeepAgent-style tool discovery for VCO: propose a minimal skill/tool chain (with verification points) and reduce confirm_required friction.
Teams using deepagent-toolchain-plan should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/deepagent-toolchain-plan/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How deepagent-toolchain-plan Compares
| Feature / Agent | deepagent-toolchain-plan | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
DeepAgent-style tool discovery for VCO: propose a minimal skill/tool chain (with verification points) and reduce confirm_required friction.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# DeepAgent Toolchain Plan (VCO) ## When to use Use this skill when: - VCO router returns `route_mode=confirm_required` and you want a **better, evidence-backed choice** - The task spans multiple domains/tools and you need a **skill chain**, not a single skill - The user asks “用什么工具/技能最好?” / “怎么编排这套技能?” - The conversation is long or messy and you need to **re-anchor** on goal → toolchain → verification ## Non-goals (avoid redundancy) - This is **not** a replacement for VCO routing. It is an *augmentation* that proposes a chain. - This is **not** GitNexus. For code dependency/impact, use GitNexus overlays. - This does **not** introduce long-term episodic memory (VCO governance disables it). ## Runtime (Upstream vendoring) DeepAgent 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.json` - `pwsh C:\Users\羽裳\.codex\skills\vibe\scripts\ruc-nlpir\preflight.ps1` ## Core output (must) Return a toolchain with: 1. **Goal + deliverable** (1–2 lines) 2. **Chain steps** (3–8 steps, each: skill/tool + why + expected artifact) 3. **Verification points** (at least 1 falsifiable check) 4. **Fallbacks** (what to do if a tool is unavailable) ## Workflow ### Step 1: Capture the task in a contract - Goal (one sentence) - Deliverable (code / plan / report / dataset / etc.) - Constraints (time, no heavy deps, offline-only, etc.) ### Step 2: Ask VCO router for a white-box view (recommended) 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-probes` Then use the emitted `confirm_ui` + overlay advice to decide the chain. ### Step 3: Build a minimal chain (DeepAgent principle) Prefer a chain that: - Starts with **evidence acquisition** (local docs / web / code graph) - Then **planning** - Then **execution** - Ends with **verification + review** ### Step 4: Guardrails - If the chain requires web browsing, explicitly choose between: - `web.run` (fast structured browse) - `playwright` / `turix-cua` (dynamic/interactive) - If the chain requires heavy model hosting (vLLM), provide a Lite alternative. ## Suggested chains (templates) ### A) “Research → report” 1. `webthinker-deep-research` (Lite) → `outputs/webthinker/.../report.md` 2. `flashrag-evidence` (local protocol checks) → citeable snippets 3. `code-reviewer` (if code changes) or `verification-quality-assurance` (if routing changes) ### B) “VCO enhancement work (config/skills)” 1. `flashrag-evidence` (locate existing policy/overlays) 2. `writing-plans` (implementation plan with file paths + verify steps) 3. `verification-before-completion` (run check + router probe)
Related Skills
writing-plans
Use when you have a spec or requirements for a multi-step task, before touching code
treatment-plans
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
speckit-plan
Generate technical implementation plans from feature specifications. Use after creating a spec to define architecture, tech stack, and implementation phases. Creates plan.md with detailed technical design.
planning-with-files
Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.
deepagent-memory-fold
DeepAgent-style memory folding for VCO sessions: compress long context into structured working/tool memory without using episodic-memory.
create-plan
Create a concise plan. Use when a user explicitly asks for a plan related to a coding task.
zinc-database
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
yeet
Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).
xlsx
Spreadsheet toolkit (.xlsx/.csv). Create/edit with formulas/formatting, analyze data, visualization, recalculate formulas, for spreadsheet processing and analysis.
xan
High-performance CSV processing with xan CLI for large tabular datasets, streaming transformations, and low-memory pipelines.
writing-docs
Guides for writing and editing Remotion documentation. Use when adding docs pages, editing MDX files in packages/docs, or writing documentation content.