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.
git clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-skillsWrote 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/lzy599775/agent-auto-sci-skills/protocol_architect_agent)<a href="https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/protocol_architect_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/protocol_architect_agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/protocol_architect_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/protocol_architect_agent.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00027 | $0.01330 |
| Opus 5.5 | $0.00011 | $0.00532 |
| Sonnet 5.5 | $0.00005 | $0.00266 |
| Haiku 4.5 | $0.00003 | $0.00133 |
Grade A, and why
protocol_architect_agent 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 4d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Protocol Architect Agent — Screening Protocol (Phase 0)
Turns whatever the user has (proposal, PROSPERO record, grant text, or criteria in their head) into two files the rest of the pipeline can apply mechanically:
screening_protocol.md: the text every reviewer receives verbatim (template:references/protocol_template.md);screening_config.json: codes and their order, core criteria, personas, seeds, QC keyword groups, models (template:templates/screening_config.template.json).
Runs in the main session, in conversation with the user. Nothing is screened until the user confirms both files.
Inputs
- Proposal or protocol:
.docx(extract the text withpython -c "import zipfile,re;..."or pandoc), PDF (Read tool), PROSPERO text, or the user's description. - Optional: known eligible studies (seeds), the databases searched, language or date limits, the team's expected number of included studies.
Procedure
-
Name the review type, because it fixes which elements the criteria need:
Review type Framework Elements Intervention PICO(S) population, intervention, comparator, outcomes, study design Diagnostic test accuracy PIRD / PICO-D participants, index test, reference standard, target condition Prognosis / prediction PICOTS population, index factor or model, outcome, timing, setting Exposure / aetiology PECO population, exposure, comparator, outcome Qualitative SPIDER or PICo sample, phenomenon of interest, design, evaluation, research type Scoping PCC population, concept, context -
Copy the criteria from the source. Keep the source's wording where it is precise. ⚠️ IRON RULE: never invent a criterion the source does not support. If a criterion is needed but missing (for example, the source never says whether animal studies count), write it as
[proposed]and ask the user. Unconfirmed criteria never reach the reviewers.
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.
- 4d ago First seen · 106 lines · 27 tokens per session scan A 99dd7482340e
protocol_architect_agent is an agent published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed yesterday), licensed MIT. It adds 27 tokens to every session and 1,330 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-12.
Other agents, from other repositories
protocol_architect_agent
Turns a proposal or protocol into confirmed, stage-split screening rules and a screening config before any record is screened.
reporter_agent
Builds the screening deliverables (log, RIS groups, PRISMA counts, methods draft, corpus handoff) from complete decisions.
compliance_agent
Runs PRISMA-trAIce + RAISE compliance checks at Stage 2.5 / 4.5 integrity gates and emits Schema 12 compliancereport.
formatter_agent
Formats the final manuscript output to target journal style requirements.
visualization_agent
Generates publication-quality figure specifications and chart descriptions for inclusion in the paper.
intake_agent
Conducts the paper configuration interview and produces the Paper Configuration Record for downstream agents.