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 chandrudp29/skillhub --skill research-agentgit clone --depth 1 https://github.com/chandrudp29/skillhubWrote 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/chandrudp29/skillhub/research-agent)<a href="https://agentmods.dev/skills/chandrudp29/skillhub/research-agent"><img src="https://agentmods.dev/badge/skills/chandrudp29/skillhub/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/chandrudp29/skillhub/research-agent"><img src="https://agentmods.dev/badge/skills/chandrudp29/skillhub/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 | $0.00036 | $0.00866 |
| Opus 5.5 | $0.00014 | $0.00346 |
| Sonnet 5.5 | $0.00007 | $0.00173 |
| Haiku 4.5 | $0.00004 | $0.00087 |
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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Agent
Produces thorough, cited research reports from multiple sources. Not a search wrapper — a research analyst.
When to Use
- "Research X for me"
- "Compare A vs B"
- "What's the current state of X?"
- "Competitive analysis of X"
- "Should we use X or Y?"
Workflow
Step 1 — Clarify scope (30 seconds max)
Ask exactly one question if the goal is ambiguous:
- Decision context ("choosing a tool") → focus on tradeoffs
- Learning context ("understand X") → focus on concepts and examples
- Writing context ("write a report") → focus on structure and evidence
If obvious, skip clarification and proceed.
Step 2 — Decompose into sub-questions
Break the topic into 3–5 research sub-questions. Think like a research analyst:
Example: "Should we use Pinecone or Weaviate?"
- What are the performance benchmarks at 10M+ vectors?
- What are the pricing models at scale?
- What are the operational differences (managed vs self-hosted)?
- What does the developer community say (GitHub issues, Reddit, HN)?
- What are the migration paths if we need to switch?
State the sub-questions before researching. This prevents rabbit holes.
Step 3 — Search each sub-question
For each sub-question, run 2–3 searches with different keyword angles:
- Exact name searches
- Comparison searches ("X vs Y")
- Recent news searches ("X 2025" or "X latest")
- Community searches ("X reddit" or "X hacker news")
Prioritize sources: official docs > peer-reviewed > reputable tech press > community discussion > personal blogs.
Step 4 — Read primary sources
Don't rely on summaries. For each key claim, read the actual source:
- Official documentation for feature claims
- Benchmark methodology for performance claims
- GitHub issues for real-world problems
- Pricing pages for cost claims (pricing pages change — note the date)
Step 5 — Synthesize with citations
Structure the output:
## Summary (3–5 sentences, the answer upfront)
## Key Findings
- Finding 1 [Source: URL, Date]
- Finding 2 [Source: URL, Date]
## Deep Dive: [Sub-question 1]
...content with inline citations [1]...
## Deep Dive: [Sub-question 2]
...
## Comparison Table (if applicable)
| Criterion | Option A | Option B |
|-----------|----------|----------|
## Recommendation
Clear recommendation with reasoning. State confidence level.
## Sources
[1] URL — description — accessed YYYY-MM-DD
What ships with it
1 file 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 · 110 lines · 36 tokens per session scan A 760626fecbde
research-agent is a skill published in the GitHub repository chandrudp29/skillhub (14 stars, last pushed 3mo ago), licensed MIT. It adds 36 tokens to every session and 866 once invoked, about $0.0001 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.
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