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 commands/osmantic/ods/deep-researchgit clone --depth 1 https://github.com/Osmantic/ODSWhat 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 | $0.00021 | $0.05732 |
| Opus 5 | $0.00010 | $0.02866 |
| Sonnet 5 | $0.00004 | $0.01146 |
| Haiku 4.5 | $0.00002 | $0.00573 |
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.
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.
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 · 620 lines · 21 tokens per session scan A f4c2a4fc1bc3
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.
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