Agentero is a local research workspace that connects papers, structured reading material, Markdown notes, and AI agents in one place. Researchers use it to read and annotate PDFs, extract formulas and figures, organize literature, and collaborate with agents on research tasks while keeping context locally. Its catalogue skills and instruction support agent-assisted research workflows in Agentero.
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.
npx skills add poco-ai/Agentero --skill deep-researchgit clone --depth 1 https://github.com/poco-ai/AgenteroWrote 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/skills/poco-ai/agentero/deep-research)<a href="https://agentmods.dev/skills/poco-ai/agentero/deep-research"><img src="https://agentmods.dev/badge/skills/poco-ai/agentero/deep-research/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/skills/poco-ai/agentero/deep-research"><img src="https://agentmods.dev/badge/skills/poco-ai/agentero/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 60 Skill instructs the agent to never refuse or to always comply. Suppressing the agent's ability to decline removes a core safety control and enables downstream harmful requests to succeed.Fix: Remove any instruction telling the agent to never refuse or always comply. The agent must retain the ability to decline unsafe, out-of-scope, or harmful requests.
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 | $0.00091 | $0.02037 |
| Opus 5.5 | $0.00036 | $0.00815 |
| Sonnet 5.5 | $0.00018 | $0.00407 |
| Haiku 4.5 | $0.00009 | $0.00204 |
Grade A, and why
deep-research 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 1mo 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Overview
The role is a senior researcher who has written strong surveys and served as a section editor. Someone brings a research topic; the job is to map the terrain, lay out the key works, surface the tensions and the gaps, and end with a judgment the evidence has earned.
This is not a search engine and not a bullet-point answer. The deliverable reads like a real survey paper: questions defined first, evidence developed systematically, conclusions emerging from the evidence rather than announced up front and back-filled.
When to use this skill
- "Survey this direction for me." "What is the state of X?"
- "What are the key works, debates, and open problems in this area?"
- "I need a literature review before committing to this topic."
When NOT to use this skill
- Judging whether one specific idea is worth pursuing:
idea-evaluator. - Drafting or polishing paper prose:
paper-writer,intro-drafter,paper-polish. - A quick lookup of one or two papers: plain search suffices; this skill is for depth.
Capability check
- Literature search: a scholarly search tool if available; otherwise web search over scholarly indexes; otherwise shell access to public APIs (Crossref, Semantic Scholar, arXiv, DBLP). With no retrieval at all, this skill does not run: say so rather than writing a survey from memory.
- Sub-agents: when the environment can spawn parallel sub-agents, each search perspective runs as its own agent with a clean context; without them, run the perspectives serially yourself. The method does not change, only the execution.
Step 0: freeze the research brief
Before any searching, pin down what is being investigated.
The brief must contain research questions: not a topic, but two or three specific, answerable questions. They are the survey's promise; the conclusion must answer each one.
If the user gives only a topic ("survey CRISPR for me"), convert it with one or two clarifying questions:
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 211 lines · 91 tokens per session scan A 4208550b8d2b
deep-research is a skill published in the GitHub repository poco-ai/Agentero (1,032 stars, last pushed today), licensed MIT. It adds 91 tokens to every session and 2,037 once invoked, about $0.0004 per session on Opus 5.5. 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.
Other skills, from other repositories
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
aatmf-t10-confidentiality-breach
AATMF T10 — Integrity & Confidentiality Breach. System prompt extraction, training-data extraction, model-weight leakage, private-key recovery.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.
ensembl-database
Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.
ai-music
A workflow that turns a topic or script into a finished short video by combining writing, images or video clips, spoken narration, subtitles, background music, and final assembly.
skill-news-intelligence
A daily industry-news research workflow that gathers in-depth articles and updates from Chinese technology, business, creator, and AI publications. It filters the results by a creator’s field and turns them into a brief with possible content topics.