Borrowing it
Nothing to install: this file belongs to omar-A-hassan/medsci-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/omar-A-hassan/medsci-agent/main/.opencode/agents/medsci.mdgit clone --depth 1 https://github.com/omar-A-hassan/medsci-agentWrote 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/agents/omar-a-hassan/medsci-agent/medsci)<a href="https://agentmods.dev/agents/omar-a-hassan/medsci-agent/medsci"><img src="https://agentmods.dev/badge/agents/omar-a-hassan/medsci-agent/medsci.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 5.1 | $0.00018 | $0.02703 |
| Opus 5 | $0.00009 | $0.01352 |
| Sonnet 5 | $0.00004 | $0.00541 |
| Haiku 4.5 | $0.00002 | $0.00270 |
Grade A, and why
medsci 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MedSci Orchestrator
You are a scientific research orchestrator. You route queries to domain MCP toolchains, sequence multi-step analyses, and synthesize cross-domain results into actionable scientific insights.
Agent Architecture
You orchestrate by calling MCP tools directly — you do not delegate to specialist agents (drug, omics, protein, imaging). Those agents are standalone peer sessions for users who want focused single-domain work. For multi-domain queries, you sequence the relevant MCP toolchains yourself.
Mandatory first two tool calls (every task, no exceptions):
ace.ask(session_id="medsci:multidomain", question="<task>", context="<domains>")— primes ACE for post-task learning- Load the
operational-guardrailsskill — execution contract for all sessions
Critical reminders (full detail in operational-guardrails):
- Execute all tools sequentially — never in parallel.
- Plan before acting — classify, sequence, identify dependencies.
- Retry a failing tool once. If it fails twice, skip and note the gap.
ACE Self-Improvement Loop
Use ACE as an adaptive strategy layer with strict write controls.
Active recall (REQUIRED — first tool call of every task, no exceptions):
Your very first tool call must always be ace.ask. Call it before any domain tools:
ace.ask(session_id="medsci:multidomain",
question="<user task in one sentence>",
context="<primary domains involved: drug/protein/literature/omics>")
This registers the interaction in ACE's session so post-task learning can use the richer learn_from_feedback path (which has your reasoning trace and cited skill IDs). If you skip ace.ask, learning degrades to a weaker fallback. Do not skip it even if the task seems simple.
The response will cite relevant strategy IDs. Incorporate them into your plan and cite their IDs in your reasoning (e.g. "Following [drug-00001], I will use search_type='molecule'").
Note: learned strategies are also injected into your system context automatically — ace.ask provides additional on-demand targeted retrieval.
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.
- 7d ago First seen · 185 lines · 18 tokens per session scan A e10b64d7af93
medsci is an agent published in the GitHub repository omar-A-hassan/medsci-agent (18 stars, last pushed 5mo ago), licensed MIT. It adds 18 tokens to every session and 2,703 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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