Wanwu is an enterprise platform for building AI agents, workflows, retrieval-augmented applications, and managing models in multi-tenant environments. It is designed for developers and enterprise teams delivering AI applications and integrations. The catalogue entries provide skills and agents for using the platform.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/unicomai/wanwu/indication-dossiernpx skills add UnicomAI/wanwu --skill indication-dossiergit clone --depth 1 https://github.com/UnicomAI/wanwuWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/unicomai/wanwu/indication-dossier)<a href="https://agentmods.dev/skills/unicomai/wanwu/indication-dossier"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/indication-dossier.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00034 | $0.01357 |
| Opus 5 | $0.00017 | $0.00678 |
| Sonnet 5 | $0.00007 | $0.00271 |
| Haiku 4.5 | $0.00003 | $0.00136 |
Grade A, and why
indication-dossier scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to indication-dossier — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Indication Dossier
Produces a structured research dossier on a single indication, framed as a patient population: who they are, what's wrong, how they're treated today, and how clinical trials can be designed to help them. Runs as five phases that write resumable waypoint files; after a brief identity check at the end of Phase 1, the remaining phases run straight through.
Framing
Think of an indication as a patient population. Frame everything from the patient perspective: "Who are these patients?" not "What is this disease?"; "How are these patients identified and managed?" not "What causes this condition?"; population nesting: "all patients in {child} are patients in {parent}".
Some indications don't map to ICD codes or standard disease definitions: "immunosenescence" is a biological state, not a billable diagnosis; "ageing" is not an FDA-accepted indication; "GLP-1 induced sarcopenia" is an iatrogenic population. Note these distinctions explicitly. They matter for regulatory path and trial design.
Inputs
indication(required) — indication name (e.g., "sarcopenia", "idiopathic pulmonary fibrosis").additional_context(optional) — areas to focus on, parent indication, or other framing.workdir(optional) — where to write waypoints and the final report. Defaults to./do_not_commit/indication-dossier-<slug>/.
Tools this skill expects
| Purpose | Tool |
|---|---|
| ClinicalTrials.gov | clinical-trials MCP |
| Literature | pubmed MCP |
| Web | WebSearch, WebFetch — FDA guidance, treatment guidelines (NCCN, AASLD, specialty societies), CDC/WHO epidemiology data |
| Documents | WebFetch for remote PDFs; Read for local PDFs |
| Subagents | Agent for parallel evidence gathering |
If a listed MCP isn't connected, say so and fall back to WebSearch against
the underlying public source (clinicaltrials.gov, pubmed.ncbi.nlm.nih.gov).
Output layout
<workdir>/
└── waypoints/
├── progress.json # loop control
├── meta.json # phase 1
├── epidemiology.json # phase 2
├── biology_soc.json # phase 3
├── regulatory_trials.json # phase 4
├── sources_evaluated.json
├── research_output.json # phase 5 — structured output
└── indication_dossier_report.md # phase 5 — the deliverable
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- .catalog_stamp 13 B
- references/00-research-standards.md 3.0 KB
- references/01-meta-initialization.md 1.7 KB
- references/02-epidemiology-research.md 2.1 KB
- references/03-biology-soc-research.md 2.3 KB
- references/04-regulatory-trials-research.md 3.1 KB
- references/05-synthesis.md 5.2 KB
- references/06-writing-style.md 1.7 KB
- references/waypoint-schemas.md 2.9 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 136 lines · 34 tokens per session scan A dfad982f985e
indication-dossier is a skill published in the GitHub repository UnicomAI/wanwu (2,456 stars, last pushed yesterday), licensed Apache-2.0. It adds 34 tokens to every session and 1,357 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to indication-dossier, differing in 14 lines, and is treated as a copy.
Other skills, from other repositories
molecular-rag
Retrieve structurally similar compounds with known properties from ChEMBL/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).
sciverse-paper-search
Use this skill for scientific literature search, evidence retrieval, paper metadata screening, and cited research synthesis with Sciverse. This LazyLLM-adapted version supports SciverseSearch search, metasearch, metacatalog, and getcontent only; it does not assume full Sciverse MCP resource or attachment APIs are…
paper-search
Primary skill for searching, retrieving, and reading academic papers from arXiv.
dify
Use when building LLM applications with visual workflow — RAG knowledge bases, AI agents, chatbots with drag-and-drop orchestration. Dify: open-source LLM app platform supporting 30+ models (OpenAI, Claude, DeepSeek, Ollama, Qwen, GLM) with Docker deployment.
ai-skills
Use when building LLM applications, RAG knowledge bases, AI agents, terminal coding agents, multi-model orchestration, plugin-based agent harnesses, or file translation. Index of 9 skills: Dify, Hermes Agent, OpenClaw, OpenCode, Pi, DocuTranslate, Oh-My-OpenAgent, Superpowers-zh, DeepSeek Harness.
Web2Skill
Convert one public website URL or an explicit batch of public URLs into a reusable skill zip backed by rendered HTML snapshots and a bounded JSONL retrieval index. Use to discover a documentation directory from one URL, crawl a supplied URL set sequentially, generate a source profile, or package indexed web content as…