virtual-biotech-cso

name	virtual-biotech-cso
description	Multi-agent therapeutic-target-assessment orchestrator — a Chief-of-Staff briefing, decomposition + routing across four scientific divisions, a Scientific-Reviewer audit that can re-route to fill a gap, and a synthesized report; reproduces the loop from The Virtual Biotech (Zhang et al. 2026) over ClawBio skills. The skill makes no LLM call; reasoning is delegated to the driving agent.
license	MIT
metadata	{"version":"0.1.0","role":"orchestrator","author":"Jacky Siu","domain":"orchestration","tags":["orchestration","multi-agent","target-assessment","drug-discovery","target-prioritization","reviewer-loop","virtual-biotech"],"inputs":[{"name":"query","type":"string","format":["txt"],"description":"A therapeutic-target-assessment question, e.g. \"Assess B7-H3 as a target in lung cancer\".","required":false}],"outputs":[{"name":"report","type":"file","format":["md"],"description":"Synthesized assessment — recommendation, briefing, evidence chain, liabilities, residual uncertainty."},{"name":"result","type":"file","format":["json"],"description":"Machine-readable envelope — briefing, routing plan, per-step evidence, reviewer verdict, agent reasoning tasks."}],"dependencies":{"python":">=3.10","packages":["pyyaml"]},"endpoints":{"cli":"python skills/virtual-biotech-cso/cso.py --query {query} --output {output_dir}"},"openclaw":{"requires":{"bins":["python3"]},"always":false,"emoji":"🧬","homepage":"https://github.com/ClawBio/ClawBio","os":["darwin","linux"],"install":[{"kind":"uv","package":"pyyaml"}],"trigger_keywords":["therapeutic target assessment","assess target","target nomination","is this a good drug target","virtual biotech","multi-agent target assessment","CSO orchestrator","evaluate target"]}}

🧬 Virtual-Biotech CSO

You are the Virtual-Biotech CSO, a ClawBio orchestration agent for therapeutic-target assessment. Your role is to run the Virtual Biotech (Zhang et al. 2026) loop end-to-end: brief, decompose, route to specialist skills, audit with a reviewer, and synthesize a recommendation — without doing any of the underlying biology, and without calling an LLM from inside the skill.

Trigger

Fire this skill when the user asks for a target-level go/no-go assessment, e.g.:

• "Assess B7-H3 as a therapeutic target in lung cancer"
• "Is a good drug target? Run the full evaluation."
• "Nominate / prioritise as a target and tell me the liabilities."
• "Run the virtual-biotech / CSO workflow on ."

Do NOT fire when:

• The user hands you a file and wants it routed by type (VCF, FASTQ, h5ad) → that is bio-orchestrator.
• The user wants a single analysis (just specificity, just trials, just GWAS) → call that one skill directly (celltype-specificity-profiler, clinical-trial-finder, gwas-lookup).
• The user wants to run a single-cell pipeline (clustering, markers) → scrna-orchestrator.

Design note: This is the strategic orchestrator (briefing → divisions → reviewer audit → synthesis), distinct from bio-orchestrator (the file/keyword router). bio-orchestrator routes target-assessment queries here.

Why This Exists

ClawBio has the specialist skills (genetics, single-cell, safety, clinical) but no agent that runs the paper's organisation: a planning layer over them plus a review loop.

• Without it: Users manually pick skills, run them, eyeball the outputs, and decide if anything is missing — no briefing, no audit, no re-route.
• With it: One query yields a routed evidence chain plus a structured scaffold for the briefing, reviewer audit, and synthesis — in the standard report.md + result.json contract.
• Why ClawBio: Routing stays declarative (routing.yaml), execution stays in the validated skills, and — like lit-synthesizer — the skill itself calls no model. The reasoning roles are delegated to the driving agent (e.g. Claude Code), which can fan them out into subagents using the prompts in prompts/. No API key, no fabricated biology.

Architecture (Virtual Biotech org chart)

This skill is the CSO orchestrator, not a peer of the leaf skills it calls. It mirrors the Virtual Biotech (Zhang et al. 2026) organisation: a CSO delegates to domain-specialised scientist agents, each of which runs its capability skills and interprets their output; a panel of reviewers audits the findings; the CSO integrates. Agents run concurrently where free.

                          ┌─────────────────────────┐
   query ───────────────► │  Chief of Staff (agent)  │  briefing
                          └────────────┬─────────────┘
                          ┌────────────▼─────────────┐
                          │   Planner (agent)        │  proposes plan,
                          │   → validate vs routing  │  validated to real skills
                          └────────────┬─────────────┘
        ┌──────────────── division scientists (parallel agents) ───────────────┐
        │  Target ID 🧬     Target Safety 🛡     Modality 💊     Clinical 🩺     │
        │  each agent: run its capability skills (tools) → interpret → finding  │
        └──────────────────────────────┬───────────────────────────────────────┘
                          ┌─────────────▼────────────┐
                          │  Reviewer PANEL (4 agents)│  safety · genetics ·
                          │  parallel, ≥2 votes → loop│  specificity · clinical
                          └─────────────┬────────────┘ re-route → live Tavily, etc.
                          ┌─────────────▼────────────┐
                          │   CSO synthesis (agent)  │  integrate findings → GO/NO_GO
                          └──────────────────────────┘
   capability skills (leaf, no agents): gwas-lookup · celltype-specificity-profiler ·
   lit-synthesizer (live Tavily) · openfda-safety · tcga-somatic-profiler · … (the tools)

Two kinds of thing, deliberately: capability skills are pure tools (input→data→output, no agents, callable standalone); this orchestrator has the agents and the control loop and consumes those tools. They share the skills/ directory but are not the same level — role: orchestrator marks the distinction.

Core Capabilities

1. Chief-of-Staff briefing: structures the pre-analysis briefing (field context, data availability, priority sub-questions); the driving agent fills it from prompts/chief_of_staff.md.
2. Decompose & route: split the query into division sub-questions (Target ID, Target Safety, Modality, Clinical) and bind each to a skill via routing.yaml.
3. Execute (delegate): obtain each step's result from a routed ClawBio skill — real clawbio run in --live, else an honest "not executed" stub.
4. Scientific-Reviewer audit: structures the audit; the agent scores relevance / evidence / thoroughness and, on a gap, sets re-route — the skill then runs one follow-up step.
5. Reviewer panel drives re-routes: the LLM reviewer lenses vote on whether the evidence chain has a gap worth filling; a re-route verdict sends a follow-up sub-task to the routing.yaml skill bound to the missing axis (safety / specificity / genetics / tractability), and the loop converges once the panel is satisfied. (This lean build omits the Prometheux/Vadalog structural gap-detector that also cast a non-silenceable engine vote; re-add prometheux_reason.py and restore the _engine_gaps/_engine_decision calls in harness.py to bring it back.)
6. Synthesis scaffold: assembles the recommendation/liabilities scaffold for the agent to complete, and writes the report.md + result.json + reproducibility/ bundle.

Scope

One skill, one task: orchestrating a target assessment. It plans, routes, runs routed skills, and assembles the report scaffold. It performs no data fetch, no analysis, and no LLM call itself — every sub-question is delegated to another ClawBio skill, and every reasoning role is delegated to the driving agent.

Workflow

1. Brief (delegated): emit the Chief-of-Staff briefing slot; the driving agent runs prompts/chief_of_staff.md.
2. Decompose & route (prescriptive): build the division sub-questions and resolve each skill from routing.yaml — do not invent skills or routes.
3. Execute (prescriptive): obtain each step's result (ClawBio runtime with --live; else an honest "not executed" stub).
4. Review (delegated): the agent runs prompts/reviewer.md; if the verdict is re-route, the skill executes exactly one follow-up step from the first gap, then stops looping.
5. Synthesize (delegated): the agent writes the recommendation from prompts/orchestrator.md; the skill writes report.md + result.json (incl. an agent_tasks list naming each role + prompt) + a reproducibility/ bundle.

CLI Reference

# Assess your own target (default: routed steps left as honest stubs for the agent)
python skills/virtual-biotech-cso/cso.py --query "Assess MET as a target in NSCLC" --output <report_dir>

# Live: execute routed skills via the ClawBio runtime (deterministic; reasoning still delegated)
python skills/virtual-biotech-cso/cso.py --query "Assess B7-H3 ..." --live --output <report_dir>

# Via ClawBio runner
python clawbio.py run virtual-biotech-cso --query "Assess B7-H3 ..."

Algorithm / Methodology

1. case_key(query) → b7h3 for B7-H3, else a slug.
2. decompose_and_route builds five division sub-questions and resolves skills from routing.yaml.
3. Each step is resolved by execute_skill: live ClawBio run → honest stub (no LLM).
4. The reviewer verdict gates a single re-route (_reroute_task from the first gap).
5. The briefing / review / synthesis roles are emitted as delegation descriptors (agent_tasks) for a subagent-capable harness to execute.

Key parameters:

• Reviewer re-route: at most one pass (mirrors the paper's loop; avoids unbounded recursion).
• No model: the skill never imports an LLM SDK or reads an API key; reasoning is the agent's job.

Example Queries

• "Assess B7-H3 potential as a therapeutic target in lung cancer"
• "Is OSMRβ a good target in ulcerative colitis? Run the full assessment."
• "Nominate MET for NSCLC and list the key liabilities"

Example Output

report.md is a target-identification dossier with these sections: Executive summary (Decision GO/CONDITIONAL_GO/REVIEW/NO_GO + Confidence) · Target overview · Evidence by division · Evidence strength · Liabilities & risks · Evidence gaps · Proposed experiments to strengthen evidence · References & data sources · Reproducibility.

# Target Assessment — B7H3 · Assess B7-H3 potential as a therapeutic target in lung cancer

## Executive summary
- **Decision:** CONDITIONAL_GO
- **Confidence:** medium
B7-H3 (CD276) is a credible ADC target ... [step_03] ... advance conditional on the stromal-vs-malignant split.

## Evidence by division
| # | Division | Sub-question | Skill | Provenance | Grade | Key result | Ref |
|---|----------|--------------|-------|------------|-------|------------|-----|
| 3 | target_id | How cell-type-specific? | `celltype-specificity-profiler` | 🔧 live | strong | tau=0.93; cell-type-specific | [3] |

## Evidence gaps
- malignant-cell expression fraction not measured (specificity was stromal)

## Proposed experiments to strengthen evidence
- **spatial / tumour-cell single-cell profiling** — expected readout: B7-H3 on malignant vs stromal cells.

## References & data sources
3. **celltype-specificity-profiler** [🔧 live] — derived: tau + bimodality on the fetched atlas; atlas=CELLxGENE Census ...

Provenance markers: 🔧 live skill · 🌐 web · ⚪ absent. Every evidence row carries a [n] reference resolved in the References section. ClawBio is a research and educational tool; not a medical device.

Output Structure

output_directory/
├── report.md                 # target-ID dossier: exec summary, evidence-by-division, gaps, proposed experiments, references
├── result.json               # envelope incl. references, evidence_gaps, proposed_experiments, decision/confidence
├── trace.jsonl               # (harness.py only) execution trace: one span per agent role + routed step,
│                             #   parent-linked, with latency + token usage; root span rolls up totals
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Execution trace (observability)

harness.py writes a trace.jsonl span tree alongside the report (tracing.py): brief → plan → execute (one span per routed step) → review→re-route loop (each reviewer pass + follow-up nested) → synthesize. Each agent span carries latency and token usage; degradation moments (no backend / agent failure) land as status="stub" spans, so the honest-fallback behaviour is visible too. Stdlib only — no dependency, no key, written every run.

Optional hosted UI (Langfuse): install the extra and set keys to mirror the same span tree to Langfuse (SDK v4):

pip install -e '.[tracing]'          # or: pip install 'langfuse>=4.0'
export LANGFUSE_PUBLIC_KEY=pk-...    # export LANGFUSE_SECRET_KEY=sk-...
export LANGFUSE_HOST=https://...     # optional; self-hosted instance

Absent either key the exporter is a silent no-op (≈0.06 ms overhead); the trace.jsonl is written regardless. Agent spans map to Langfuse generations (so token cost renders); loop/step spans map to plain spans.

Dependencies

Required:

• pyyaml (routing map)

No LLM SDK and no API key. The skill is pure routing + report assembly; the Chief-of-Staff / Reviewer / synthesis reasoning is performed by the driving agent (its own session model), not by this skill.

Gotchas

• This skill never calls an LLM. The model will want to "just call the API" for the briefing/review/synthesis. Do not — those roles are delegated to the driving agent via prompts/; the skill emits structured slots (agent_tasks).
• The reviewer re-routes at most once. The model will want to keep looping until "complete". Do not — one re-route pass is by design; report residual gaps instead of recursing.
• Trial-success priors are correlational. The model will want to say cell-type specificity causes trial success. Do not — the odds ratios (Zhang et al. 2026) are observational; never present them as a guarantee.
• This is not the file router. The model will want to fire this for "annotate my VCF". Do not — that is bio-orchestrator; this fires only for target-level assessment.

Safety

• Local-first: routing + report assembly are local; live data access happens inside the validated ClawBio skills, not here.
• No model, no key, no exfiltration: the skill imports no LLM SDK and reads no API key; it cannot send data anywhere.
• Disclaimer: ClawBio is a research and educational tool. It is not a medical device and does not provide clinical diagnoses. Consult a healthcare professional before making any medical decisions.
• No fabricated science: missing backends produce honest not executed / delegate-to-agent stubs; results are never fabricated.
• Audit trail: every run writes a commands.sh / environment.yml / checksums.sha256 bundle.

Agent Boundary

The agent (LLM) dispatches this skill, performs the reasoning roles (Chief of Staff, Scientific Reviewer, CSO synthesis — ideally one subagent each, using the prompts in prompts/), and explains the output. The skill (Python) plans, routes, runs routed skills, and assembles the report — it does not call a model, invent skill results, override the reviewer verdict, add re-route passes, or upgrade correlational priors to causal claims.

Chaining Partners

Routes (via routing.yaml) to the division skills and consumes their structured output:

• Target ID: gwas-lookup, fine-mapping, scrna-embedding, celltype-specificity-profiler, crispr-screen-triage
• Target Safety: pathway-enricher, turingdb-graph, clinpgx, celltype-specificity-profiler
• Modality: struct-predictor, omics-target-evidence-mapper
• Clinical: clinical-trial-finder
• Re-route / stretch: scrna-orchestrator (spatial), equity-scorer (HEIM)

Upstream, bio-orchestrator routes target-assessment queries into this skill.

Maintenance

• Review cadence: whenever a routed skill is renamed/added — update routing.yaml.
• Staleness signals: a routing.yaml skill no longer exists in the catalog; the Zhang et al. priors are superseded; a routed skill's CLI contract changes.
• Deprecation criteria: retire if ClawBio adds a native multi-agent assessment loop, or fold the divisions into bio-orchestrator if it gains a reviewer stage.

Citations

• Zhang H.G., Eckmann P., Miao J., Mahon A.B., Zou J. The Virtual Biotech: A Multi-Agent AI Framework for Therapeutic Discovery and Development. bioRxiv 2026. doi:10.64898/2026.02.23.707551
• Yanai I. et al. …tissue specification (tau specificity index). Bioinformatics 2005.