deep-research

A research command that searches the web and combines analyses from several language models into one report. It can investigate a question at shallow, medium, or deep depth and include sources and confidence assessments.

In plain words
What is it for?
Use it to research a topic, choose the search depth and number of models, and either print the report or save it to a file.
Why use it?
It reduces the need to gather sources and compare separate analyses by hand. Cross-checking multiple models can highlight agreement and uncertainty.

Command for Claude Code

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 commands/osmantic/ods/deep-research
Clone the repo
git clone --depth 1 https://github.com/Osmantic/ODS

Made for: Claude Code.

Per session 21 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 5,732 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 $0.00021 $0.05732
Opus 5 $0.00010 $0.02866
Sonnet 5 $0.00004 $0.01146
Haiku 4.5 $0.00002 $0.00573

Measured 2d ago against content hash f4c2a4fc1bc3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

deep-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 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.

.claude/commands/deep-research.md · 620 lines

How it starts

The opening of the file, as written. The whole thing — 620 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Research with Multi-Model Consensus

Conduct comprehensive research on any topic by combining real-time web search (via Rube/Composio MCP) with multi-model deep analysis and consensus synthesis (via PAL MCP). Produces a structured research report with sourced findings, cross-validated analysis, and confidence assessments.

Arguments

  • $ARGUMENTS - Research configuration:
    • First positional argument: The research topic, question, or area of investigation
    • --depth <level> - Research depth: shallow (quick overview, 2-3 searches), medium (balanced, 5-7 searches, default), deep (exhaustive, 10+ searches with follow-up queries)
    • --output <filepath> - Save the final report to a file (default: print to console)
    • --models <N> - Number of models for consensus analysis (default: 3, min: 2, max: 5)

Philosophy: Ask Early, Ask Often

This skill should liberally use AskUserQuestion at every decision point. Research is inherently exploratory — assumptions about what the user wants are frequently wrong. The cost of asking is low; the cost of researching the wrong angle is high. Specifically:

  • Before searching — confirm the research plan and sub-questions
  • When the topic is ambiguous — clarify intent, scope, and angle
  • After initial searches — share what was found and ask about direction
  • When gaps are identified — let the user prioritize which gaps matter
  • When contradictions surface — present both sides and ask for guidance
  • When models disagree — let the user break the tie
  • Before finalizing — confirm the report meets the user's needs
  • After delivery — ask about follow-up research

The user should feel like a research partner steering the investigation, not a passive recipient of a pre-baked report.

Workflow

Phase 1: Parse Arguments and Plan Research

Extract the research topic from $ARGUMENTS. Parse optional flags:

  • Default: depth=medium, models=3, no file output
  • Identify the core question and decompose it into 3-7 sub-questions that, when answered together, provide comprehensive coverage.

Read the full file on GitHub · 620 lines

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. 2d ago First seen · 620 lines · 21 tokens per session scan A f4c2a4fc1bc3

Subscribe to this mod's changes

deep-research is a command published in the GitHub repository Osmantic/ODS (5,819 stars, last pushed yesterday), licensed Apache-2.0. It adds 21 tokens to every session and 5,732 once invoked, about $0.0001 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.