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/AlterLab-IEU/AlterLab-Academic-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/alterlab-ieu/alterlab-academic-skills/intake_agent)<a href="https://agentmods.dev/agents/alterlab-ieu/alterlab-academic-skills/intake_agent"><img src="https://agentmods.dev/badge/agents/alterlab-ieu/alterlab-academic-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 | $0.00044 | $0.02252 |
| Opus 5 | $0.00022 | $0.01126 |
| Sonnet 5 | $0.00009 | $0.00450 |
| Haiku 4.5 | $0.00004 | $0.00225 |
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 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 — 250 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 alterlab-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 alterlab-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)
When Handoff Materials Are Detected
1. Auto-populate existing parameters:
- RQ -> Extract from Research Question Brief
- Discipline -> Infer from material content
- Method -> Extract from Methodology Blueprint
- Existing materials -> Mark all available materials
2. Skip redundant questions:
- Skip Step 1 (Topic & RQ) — already available
- Skip parts of Step 8 (Existing Materials) — already available
- Still need to confirm: Paper Type, Citation Format, Output Format, Language
3. Notify the user:
"I detected that you already have alterlab-deep-research materials. The following parameters have been auto-populated:
- Research question: {RQ}
- Discipline: {discipline}
- Research method: {method}
- Existing materials: {material_list}
Please confirm whether the above information is correct. We only need a few more settings before we can begin."
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 · 250 lines · 44 tokens per session scan A d9fc15c0331d
intake-agent is an agent published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 3d ago), licensed MIT. It adds 44 tokens to every session and 2,252 once invoked, about $0.0002 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 2026-09-03.
Other agents, from other repositories
pipeline_orchestrator_agent
Orchestrates the full multi-skill academic research pipeline and manages agent handoffs across phases.
devils_advocate_reviewer_agent
Challenges core arguments and logical coherence as the devils advocate reviewer in the editorial panel.
visualization_agent
Generates publication-quality figure specifications and chart descriptions for inclusion in the paper.
perspective_reviewer_agent
Peer Reviewer 3; evaluates cross-disciplinary relevance, broader impact, and alternative interpretations.
research_question_agent
Transforms vague topics into precise, FINER-evaluated researchable questions through iterative refinement.
timeline_extraction_agent
Extracts per-source temporal facts and citation provenance into Phase 2 sidecar artifacts; activated in Phase 2 (Investigation).