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 vasilyu1983/AI-Agents-public --skill ai-deep-researchgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/ai-deep-research)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-deep-research"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-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/vasilyu1983/ai-agents-public/ai-deep-research"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.04024 |
| Opus 5 | $0.00016 | $0.02012 |
| Sonnet 5 | $0.00007 | $0.00805 |
| Haiku 4.5 | $0.00003 | $0.00402 |
Grade A, and why
ai-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 9d 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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Use this skill to design and run repeatable research workflows that gather evidence across many sources, preserve provenance, and synthesize results into decision-ready outputs.
This skill covers both native deep-research agents (ChatGPT Deep Research, Gemini Deep Research, Perplexity Deep Research, Claude with web search) and custom agentic research pipelines (planner / searcher / verifier / synthesizer split).
ASCII Flow
research question
|
v
research plan
scope + source targets + queries + stop criteria + freshness window
|
v
evidence gathering
primary sources first + source ledger + hostile-source checks
|
v
verification
isolated verifier checks claims against ledger, not researcher context
|
v
synthesis
evidence-tiered answer + citations + contradictions + unknowns
Quick Reference
| Question | Default |
|---|---|
| When to use a native agent vs custom pipeline? | Native for ad-hoc, open-ended questions. Custom for repeatable, auditable, or multi-source workflows. |
| What is the first artifact of any research task? | The source ledger — never the synthesis. |
| When is a source trustworthy? | When it is a primary document with a stable URL, author attribution, and a verifiable date. |
| What stops an unbounded research loop? | An explicit stop criterion defined before the loop starts (saturation condition or max iterations). |
| How to handle contradictory sources? | Separate them into evidence tiers; do not resolve by averaging. |
Use This Skill When
- You need to produce a sourced comparison, brief, memo, or research dossier.
- You need to choose between a native deep-research agent and a custom pipeline.
- You want to build a repeatable, auditable research workflow with provenance.
- You need to detect hostile sources, citation laundering, or model-output-as-source.
- You need to run a verifier subagent that has not seen the researcher's context.
Do Not Use This Skill For
What ships with it
13 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.
- agents/openai.yaml 321 B
- assets/templates/research-plan.template.md 2.7 KB
- assets/templates/source-ledger.template.md 2.4 KB
- data/sources.json 8.3 KB
- learnings.consolidated.md 592 B
- learnings.md 1.6 KB
- references/agentic-research-loop-architecture.md 6.1 KB
- references/anti-patterns-catalog.md 12 KB
- references/evidence-packaging.md 558 B
- references/native-deep-research-agents.md 27 KB
- references/patterns-catalog.md 7.5 KB
- references/research-workflow.md 777 B
- scripts/citation_verifier.py 11 KB runs code
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.
- 9d ago Changed · +24 lines 6d86edf4d046
- 13d ago First seen · 215 lines · 33 tokens per session scan A a6ad297090bd
ai-deep-research is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 33 tokens to every session and 4,024 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 2026-08-30.
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