research

A workflow for researching a topic with the Deep Research MCP server. It gathers sources and saves the finished research document in a project output folder.

In plain words
What is it for?
Use it to investigate a topic, analyze provided YouTube videos, compile findings, and produce an outputs/{slug}/research.md file.
Why use it?
It gives research tasks a defined process and output location instead of leaving notes and sources scattered across a session.

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/arindam200/awesome-ai-apps/research
Any agent
npx skills add Arindam200/awesome-ai-apps --skill research
Clone the repo
git clone --depth 1 https://github.com/Arindam200/awesome-ai-apps

Made for: Claude Code, Codex.

Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 317 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 $0.00078 $0.00317
Opus 5 $0.00039 $0.00159
Sonnet 5 $0.00016 $0.00063
Haiku 4.5 $0.00008 $0.00032

Measured yesterday against content hash e8e2e7764bac, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 yesterday.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

  • research — 100% identical, 0 lines differ
advance_ai_agents/deep_research_writing_agents_nebius_okahu/.agents/skills/research/SKILL.md · 30 lines

What it actually says

Research

Research a topic using the deep-research MCP server.

Working Directory

All output goes into outputs/{slug}/ relative to the project root. Derive the slug from:

  • The dataset seed filename if the user references one (e.g., my-topic_seed.mdmy-topic)
  • Otherwise, slugify the topic (lowercase, hyphens, no special chars, max 60 chars)

Create the directory if it doesn't exist.

Execution

  1. Load the research_workflow MCP prompt from the deep-research server.
  2. Follow the workflow instructions to research the user's topic using the available tools:
    • deep_research — for web research queries
    • analyze_youtube_video — for any YouTube URLs the user provides
    • compile_research — to produce the final research.md
  3. Use outputs/{slug}/ as the working_dir for all tool calls.

After Completion

Show the user the path to outputs/{slug}/research.md and a brief summary of what was found.

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. yesterday First seen · 30 lines · 78 tokens per session scan A e8e2e7764bac

Subscribe to this mod's changes

research is a skill published in the GitHub repository Arindam200/awesome-ai-apps (13,537 stars, last pushed yesterday), licensed MIT. It adds 78 tokens to every session and 317 once invoked, about $0.0004 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.