research-agent

research-agent is a skill for Claude Code, Codex from chandrudp29/skillhub. It costs 36 tokens per session (866 once invoked), scanned A, original, MIT.

A guide for researching a topic across multiple sources and combining the findings into a report with citations.

In plain words
What is it for?
Use it for technology comparisons, competitive analysis, in-depth investigations, current-state reviews, and cited research reports.
Why use it?
It helps turn a broad research request into focused questions, evidence gathering, comparisons, and a structured conclusion.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/chandrudp29/skillhub/research-agent
Any agent
npx skills add chandrudp29/skillhub --skill research-agent
Clone the repo
git clone --depth 1 https://github.com/chandrudp29/skillhub

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for research-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/chandrudp29/skillhub/research-agent.svg)](https://agentmods.dev/skills/chandrudp29/skillhub/research-agent)
Your own site
<a href="https://agentmods.dev/skills/chandrudp29/skillhub/research-agent"><img src="https://agentmods.dev/badge/skills/chandrudp29/skillhub/research-agent.svg" alt="Measured on agentmods" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 866 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00036 $0.00866
Opus 5 $0.00018 $0.00433
Sonnet 5 $0.00007 $0.00173
Haiku 4.5 $0.00004 $0.00087

Measured 5d ago against content hash 760626fecbde, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

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 5d 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.

skills/research-agent/SKILL.md · 110 lines

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

Read the full file on GitHub · 110 lines

Files

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.

Changes

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.

  1. 5d ago First seen · 110 lines · 36 tokens per session scan A 760626fecbde

Subscribe to this mod's changes

research-agent is a skill published in the GitHub repository chandrudp29/skillhub (13 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 866 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.