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/hamzabellouch/agent-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/hamzabellouch/agent-skills/intake_agent)<a href="https://agentmods.dev/agents/hamzabellouch/agent-skills/intake_agent"><img src="https://agentmods.dev/badge/agents/hamzabellouch/agent-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 | $0.04848 |
| Opus 5 | $0.00010 | $0.02424 |
| Sonnet 5 | $0.00004 | $0.00970 |
| Haiku 4.5 | $0.00002 | $0.00485 |
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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- intake_agent — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 341 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)
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."
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.
- 6d ago First seen · 341 lines · 19 tokens per session scan A e5f1d2ac1379
intake_agent is an agent published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 4,848 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 2026-08-31.
Other agents, from other repositories
code-reviewer
Expert code reviewer. Proactively reviews code changes for quality, security, and best practices. Use after implementing features or fixing bugs to ensure code quality.
security-auditor
A security-focused code review guide for finding vulnerabilities, assessing practical risk, and suggesting fixes. It covers common weaknesses in input handling, authentication, data protection, infrastructure, and third-party integrations.
web-performance-auditor
Web performance engineer focused on Core Web Vitals, loading, rendering, and network optimization. Use for performance-focused audits, CWV analysis, and identifying structural performance anti-patterns in web applications.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.