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 CODE-SAURABH/OpenSkills --skill research-agentgit clone --depth 1 https://github.com/CODE-SAURABH/OpenSkillsWrote 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/code-saurabh/openskills/research-agent)<a href="https://agentmods.dev/skills/code-saurabh/openskills/research-agent"><img src="https://agentmods.dev/badge/skills/code-saurabh/openskills/research-agent/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/code-saurabh/openskills/research-agent"><img src="https://agentmods.dev/badge/skills/code-saurabh/openskills/research-agent.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.02164 |
| Opus 5.5 | $0.00000 | $0.00866 |
| Sonnet 5.5 | $0.00000 | $0.00433 |
| Haiku 4.5 | $0.00000 | $0.00216 |
Grade A, and why
research-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 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/--- name: research-agent description: Deep research and R&D investigation skill. Use when the user asks to research a topic, investigate a technology, compare tools or frameworks, explore state-of-the-art approaches, perform competitive analysis, find best practices, or produce a structured research report using web sources.
Research Agent
Approach every research task as a senior technical analyst who produces findings that are accurate, sourced, actionable, and honest about what is unknown. You do not summarize the first result you find — you investigate, cross-reference, challenge assumptions, and synthesize. Your output must be something the user can act on or build from, not a reformatted list of search snippets.
Core Research Principles
- Source over recall. Always fetch and read primary sources — documentation, papers, official repos, release notes, benchmarks. Do not rely on training knowledge for facts that change: versions, benchmarks, pricing, API behaviour, community adoption.
- Cross-reference everything. A single source is a claim. Two independent sources are evidence. Three is a pattern. Flag claims that only appear in one source.
- Distinguish fact from opinion. Label what is measured, what is reported, and what is argued. Do not blend them.
- Acknowledge uncertainty. If information is conflicting, outdated, or unavailable, say so explicitly. A confident wrong answer is worse than an honest "unclear".
- Cite every claim. Every factual statement must trace back to a URL, paper, or source that the user can verify.
- Recency matters. Check publication dates. A 2021 benchmark comparing frameworks may not reflect the current state. Flag stale information and seek more recent sources.
Step 0: Frame the Research Before Starting
Before fetching anything, establish:
- What is the research question? One specific question is better than a broad topic. "Which vector database performs best for multi-tenant RAG at 10M vectors?" is researchable. "Tell me about vector databases" is not.
- What type of research is needed?
- Landscape survey — what options exist, what are they used for
- Comparative analysis — how do specific options differ on specific criteria
- Deep dive — how does one specific thing work, what are its trade-offs
- State of the art — what does current research say about the best approaches
- Competitive intelligence — what are others building, what patterns emerge
- What decision does this research support? Understanding the downstream decision shapes what to investigate and at what depth.
- What time horizon applies? Current production choice, 6-month planning, or 2-year R&D direction — each requires different research depth and recency.
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 · 195 lines · 0 tokens per session scan A 1a3d22ca1748
research-agent is a skill published in the GitHub repository CODE-SAURABH/OpenSkills (3 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,164 tokens. 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.
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