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 agentmods add skills/code-saurabh/openskills/research-agentnpx skills add CODE-SAURABH/OpenSkills --skill research-agentgit clone --depth 1 https://github.com/CODE-SAURABH/OpenSkillsWhat 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 | $0.00000 | $0.02164 |
| Opus 5 | $0.00000 | $0.01082 |
| Sonnet 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 2d 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.
- 2d 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 (2 stars, last pushed 1mo 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.
Other skills, from other repositories
ecommerce-landing-page
Audit and optimize e-commerce landing pages for conversion. CTA placement, trust signals, page structure, copy optimization, and A/B testing strategy for product pages, collection pages, and campaign landing pages.
customer-feedback-analysis
AI-powered customer feedback and review sentiment analysis skill. Extracts pain points, feature requests, and improvement priorities from customer reviews across e-commerce platforms.
ads-linkedin
Audit LinkedIn Ads measurement, Insight Tag and conversions, professional audiences, lead generation, ABM, creative, bidding, budgets, pacing, automation, and policy. Use for LinkedIn Ads, Campaign Manager, Insight Tag, Lead Gen Forms, Thought Leader Ads, ABM campaigns, or B2B paid media.
ads-launch
Draft or explicitly apply a paid-ad campaign launch through Claude Ads capability-gated adapters. Use for campaign creation, launch plans, publishing ads, activating campaigns, uploading creative, or requests to push a campaign live.
api-response-optimization
Optimizes API performance through payload reduction, caching strategies, and compression techniques. Use when improving API response times, reducing bandwidth usage, or implementing efficient caching.
ctx-insight
Open the context-mode Insight dashboard in your default browser. Insight is the hosted analytics layer for AI-assisted engineering teams — per-engineer productive rate, retry waste, blocker detection, role-narrowed views. Trigger: /context-mode:ctx-insight.