intake_agent

An intake agent that interviews a researcher about a planned academic paper and creates a configuration record. The record captures choices such as paper type, discipline, language, length and available materials.

In plain words
What is it for?
Use it at the start of an academic-paper workflow to define the assignment and check whether the requested settings fit together. It prepares shared instructions for the later research and writing stages.
Why use it?
It prevents missing requirements from causing problems later in the writing process. It can also identify research materials already present in the conversation.

Agent

Part of the academic-research-skills plugin — 4 skills, 10 commands, 34 agents shipped together

Install

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.

agentmods
npx agentmods add agents/lunartech-x/superpowers/intake_agent
Clone the repo
git clone --depth 1 https://github.com/LUNARTECH-X/superpowers

Or install academic-research-skills, the plugin that ships this one along with the rest of its 4 skills, 10 commands, 34 agents.

Per session 19 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,457 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00019 $0.02457
Opus 5 $0.00010 $0.01229
Sonnet 5 $0.00004 $0.00491
Haiku 4.5 $0.00002 $0.00246

Measured 3d ago against content hash 8f1c3331d4bf, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 3d 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.

Origin

Copies of this mod

3 near-identical copies found in the catalogue:

skills/academy-skills/academic-research-skills/academic-paper/agents/intake_agent.md · 271 lines

How it starts

The opening of the file, as written. The whole thing — 271 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

  1. Complete but efficient — collect all necessary parameters without over-burdening the user
  2. Smart defaults — suggest sensible defaults based on discipline and paper type
  3. Validate early — catch incompatible configurations (e.g., 2000-word IMRaD is too short)
  4. Existing materials inventory — understand what the user already has to avoid redundant work
  5. Bilingual awareness — detect user language and set defaults accordingly
  6. Handoff awareness — detect materials from deep-research and auto-import

Deep Research Handoff Detection

Step 0 (executed before the original interview flow):

Detection Logic

  1. Check the conversation context for materials produced by deep-research
  2. 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 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."

Read the full file on GitHub · 271 lines

Changes

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.

  1. 3d ago First seen · 271 lines · 19 tokens per session scan A 8f1c3331d4bf

Subscribe to this mod's changes

intake_agent is an agent published in the GitHub repository LUNARTECH-X/superpowers (16 stars, last pushed 3mo ago), licensed MIT. It adds 19 tokens to every session and 2,457 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.