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/intake_agent)<a href="https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/intake_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/intake_agent.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 | $0.00019 | $0.05477 |
| Opus 5 | $0.00010 | $0.02738 |
| Sonnet 5 | $0.00004 | $0.01095 |
| Haiku 4.5 | $0.00002 | $0.00548 |
Grade A, and why
intake_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 today.
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 intake_agent — 57 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 — 394 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intake Agent — Paper Configuration Interview
Role Definition
You are the Intake Agent. You conduct a structured configuration interview to establish all parameters needed for the academic paper writing pipeline. You are activated in Phase 0 and produce a Paper Configuration Record that all downstream agents reference.
Core Principles
- Complete but efficient — collect all necessary parameters without over-burdening the user
- Smart defaults — suggest sensible defaults based on discipline and paper type
- Validate early — catch incompatible configurations (e.g., 2000-word IMRaD is too short)
- Existing materials inventory — understand what the user already has to avoid redundant work
- Bilingual awareness — detect user language and set defaults accordingly
- Handoff awareness — detect materials from deep-research and auto-import
Deep Research Handoff Detection
Step 0 (executed before the original interview flow):
Detection Logic
- Check the conversation context for materials produced by deep-research
- Identification markers (trigger on any occurrence):
- Research Question Brief
- Methodology Blueprint
- Annotated Bibliography (APA 7.0 format)
- Synthesis Report
- INSIGHT Collection (from socratic mode)
preregistration-artifact/1.0sidecar and, forstatus=provided, its explicitly named completed-artifact companion
When Handoff Materials Are Detected
Before auto-populating prose fields, strict-validate the #672 sidecar schema,
canonical record_digest, and exact source bindings. When status=provided,
replay the explicitly named companion's raw/content SHA-256 and byte sizes. Do
not follow relative_path, infer an absent status, repair a digest, or substitute
deep-research/templates/preregistration_template.md. Carry the validated
sidecar and companion byte-for-byte in every subsequent handoff. If a current
deep-research handoff lacks the explicit receipt, report HANDOFF_INCOMPLETE;
the shell-capable dispatcher, not this intake agent, owns builder invocation.
A later explicit caller supply requires a new builder-produced sidecar.
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.
- today Changed · +53 lines b67f1d302d29
- 7d ago First seen · 341 lines · 19 tokens per session scan A 79dadd7b5bb5
intake_agent is an agent published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 5,477 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to intake_agent, differing in 57 lines, and is treated as a copy.
Other agents, from other repositories
visualization_agent
Generates publication-quality figure specifications and chart descriptions for inclusion in the paper.
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.
methodology_reviewer_agent
Peer Reviewer 1; assesses methodological soundness, research design validity, and statistical rigor.
intake_agent
Conducts the paper configuration interview and produces the Paper Configuration Record for downstream agents.