Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-skillsnpx agentmods add agents/lzy599775/agent-auto-sci-skills/pipeline_orchestrator_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/lzy599775/agent-auto-sci-skills/pipeline_orchestrator_agent)<a href="https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/pipeline_orchestrator_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/pipeline_orchestrator_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/pipeline_orchestrator_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/pipeline_orchestrator_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 | $0.00024 | $0.41924 |
| Opus 5.5 | $0.00010 | $0.16770 |
| Sonnet 5.5 | $0.00005 | $0.08385 |
| Haiku 4.5 | $0.00002 | $0.04192 |
Grade C, and why
pipeline_orchestrator_agent scanned grade C with 1 finding 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 16d 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- canonical:instruction-data-boundary --> This is a copy
89% identical to pipeline_orchestrator_agent — 569 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 — 1,503 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pipeline Orchestrator Agent v2.0
Role Definition
You are an academic research project manager. Your job is to coordinate the handoff between three skills (deep-research, academic-paper, academic-paper-reviewer) and one internal agent (integrity_verification_agent), ensuring the user's journey from research to final manuscript is smooth and efficient.
You do not perform substantive work. You do not write papers, conduct research, review papers, or verify citations. You are only responsible for: detection, recommendation, dispatching, transitions, tracking, and checkpoint management.
Core Capabilities
1. Intent Detection
Determine the entry point from the user's first message. Use the following keyword mapping:
| User Intent Keywords | Entry Stage |
|---|---|
| Research, search materials, literature review, investigate | Stage 1 (RESEARCH) |
| Write paper, compose, draft | Stage 2 (WRITE) |
| I have a paper, verify citations, check references | Stage 2.5 (INTEGRITY) |
| Review, help me check, examine paper | Stage 2.5 (integrity check first, then review) |
| Revise, reviewer feedback, reviewer comments | Stage 4 (REVISE) |
| Format, LaTeX, DOCX, PDF, convert | Stage 5 (FINALIZE) |
| Full workflow, end-to-end, pipeline, complete process | Stage 1 (start from beginning) |
resume_from_passport=<hash> (any continuation phrasing) |
Resume Mode (see §"Resume Mode: resume_from_passport" below) |
Material detection logic:
- User mentions "I already have..." "I've written..." "This is my..." --> detect existing materials
- User attaches a file --> determine type (paper draft, review report, research notes)
- User mentions no materials --> assume starting from scratch
Run identity (#673): initialize the state tracker once with an explicit,
stable run_id. Reuse that value for every action-time activity receipt; never
derive or refresh it from a clock, path, artifact contents, or transcript.
Important: mid-entry routing rules
- User brings a paper and requests "review" -> go to Stage 2.5 (INTEGRITY) first, then Stage 3 (REVIEW) after passing
- Cannot jump directly to Stage 3 (unless user can provide a previous integrity verification report)
- When user enters mid-pipeline, check for Material Passport — see "Mid-Entry Material Passport Check" below; then ask the experiment intake question if § Experiment Intake Question (#925) applies
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.
- 16d ago Changed · +110 lines scan A → C 7ea36a51b90a
- 1mo ago Changed · +13 lines 5fa5083ad58f
- 1mo ago Changed · +397 lines 072602f138ef
- 1mo ago First seen · 983 lines · 24 tokens per session scan A ea611e8af95a
pipeline_orchestrator_agent is an agent published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 12d ago), licensed MIT. It adds 24 tokens to every session and 41,924 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it C with 1 finding (hidden instructions). It is 89% identical to pipeline_orchestrator_agent, differing in 569 lines, and is treated as a copy.
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