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name: deep-research description: "Operate Tongyi DeepResearch and its WebAgent family: ReAct inference setup, rollout validation, benchmark evaluation, and variant routing." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

DeepResearch Repo Skill

Use this repo skill when a task involves Tongyi DeepResearch, Alibaba-NLP DeepResearch, Tongyi-DeepResearch-30B-A3B, root ReAct inference, DeepResearch rollout files, official benchmark scoring, or the related WebAgent/Agent family variants bundled with the project.

This skill is self-contained operating context. Use its bundled references and scripts first; do not rely on the original repository docs, examples, or scripts for routine setup, routing, and validation questions.

Fast Routing

Task signal	Read
.env, model path, local vLLM ports, OpenRouter/OpenAI-compatible inference, JSON/JSONL questions, file references, Search/Visit/Scholar/Python/File tools, <tool_call> or <answer> issues	sub-skills/react-inference/SKILL.md
iter1.jsonl, iter2.jsonl, iter3.jsonl, split rollouts, prediction schema, HLE, GAIA, BrowseComp, WebWalker, XBench DeepSearch, Pass@k, judge API costs	sub-skills/benchmark-evaluation/SKILL.md
Choosing between WebDancer, WebSailor, WebWatcher, WebWeaver, WebWalker, WebResummer/ReSum, WebShaper, WebLeaper, AgentFold, ParallelMuse, AgentFounder, AgentScaler, or NestBrowse	sub-skills/webagent-family/SKILL.md
Cross-cutting install/config prerequisites, service variables, model-family overview, or repo staleness checks	Root references and scripts below

Operating Procedure

1. Identify the user’s deliverable. Distinguish inference generation, prediction validation, official judging, family selection, or method comparison. Many user requests say “DeepResearch” but actually mean a WebAgent subproject.
2. Check source/release alignment when needed. Read references/repo-provenance.md and optionally run scripts/inspect_deepresearch_checkout.py --repo-root <checkout> against a user-provided checkout before assuming this skill matches it.
3. Validate before expensive execution. Use the bundled stdlib validators for .env, input datasets, rollout folders, and family routing before launching GPU servers, web tools, or LLM-as-judge APIs.
4. Surface prerequisites explicitly. Full local runs usually require model weights, multiple GPUs, vLLM or SGLang, search/visit/file/parser credentials, and benchmark data. Hosted OpenAI-compatible routes can avoid local GPUs but still need API credentials and code/config adaptation.
5. Keep verification honest. A schema validator pass is not the same as a successful model rollout or official judge score. Do not treat missing credentials, model weights, large image archives, or required GPUs as ordinary passes.

Setup Note

• The inspected checkout is not packaged as a normal installable Python distribution. For real local inference or benchmark execution, install requirements.txt inside a private Python 3.10 environment that the user authorizes for this task.
• For validation-only or routing-only work, the bundled stdlib scripts are enough: scripts/inspect_deepresearch_checkout.py, sub-skills/react-inference/scripts/build_react_env.py, sub-skills/react-inference/scripts/validate_deepresearch_dataset.py, sub-skills/benchmark-evaluation/scripts/validate_prediction_rollouts.py, and sub-skills/webagent-family/scripts/choose_webagent_variant.py.
• A minimal health check for the generated skill is to run the inspection helper and the relevant --help/fixture validators; there is no public import name to check at the root because the repository is a script/model-release collection rather than a Python package.

Root References

• references/model-and-repo-overview.md — project purpose, root model facts, WebAgent family relationship, and what is runnable versus method-only.
• references/configuration.md — shared Python, dependency, model, credential, dataset, and backend prerequisites.
• references/troubleshooting.md — cross-cutting install/import, credential, model-serving, path, and dependency failures.
• references/repo-provenance.md — source snapshot and evidence paths used to build this skill.
• references/repo-routing-metadata.json — structured scenario metadata for managed repo-skill routing.

Root Script

• scripts/inspect_deepresearch_checkout.py --help — safe stdlib checker for a current checkout’s expected files, Git state, large-blocker hints, and likely staleness relative to this skill’s provenance.

Important Boundaries

• Do not run full ReAct inference unless the user has provided model weights or a hosted model route, required tool credentials, output/data paths, and permission for long-running GPU/network work.
• Do not run official LLM-as-judge evaluation until rollout shape passes preflight and the user has approved API cost/credentials.
• Do not promise unreleased checkpoints, training data, Heavy Mode, or paper-only pipelines. Route them as documented limitations or method references.
• Do not place secrets in generated .env templates, logs, reports, or user-facing plans.
• Route project-maintenance edits, packaging changes, or source-code modifications through ordinary repository-maintenance reasoning; this repo skill is for operating and troubleshooting the public DeepResearch/WebAgent workflows.