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omd-research-deep

omd-research-deep is a skill for Claude Code, Codex from AbyssCN/oh-my-dag. It costs 120 tokens per session (1,054 once invoked), scanned A, original, MIT.

A deep research workflow that breaks a broad question into several viewpoints, gathers sources, checks for missing evidence, and combines the findings into a grounded report. The report includes source material and records what each research round added.

In plain words
What is it for?
Use it to investigate an entire topic, compare mechanisms and real-world practice, examine opposing views, and produce a source-backed report.
Why use it?
It helps when a quick search is not enough and you need broad coverage with evidence, follow-up searches, and an honest account of gaps.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 44repo changed 26d ago A scan SkillSpector: pass 120 tokens original MIT

Good fit Use it to investigate an entire topic, compare mechanisms and real-world practice, examine opposing views, and produce a source-backed report.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/abysscn/oh-my-dag/omd-research-deep
View source ↗ AbyssCN/oh-my-dag
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.

Any agent
npx skills add AbyssCN/oh-my-dag --skill omd-research-deep
Clone the repo
git clone --depth 1 https://github.com/AbyssCN/oh-my-dag

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 omd-research-deep

README.md
[![agentmods](https://agentmods.dev/badge/skills/abysscn/oh-my-dag/omd-research-deep/github.svg)](https://agentmods.dev/skills/abysscn/oh-my-dag/omd-research-deep)
Your own site
<a href="https://agentmods.dev/skills/abysscn/oh-my-dag/omd-research-deep"><img src="https://agentmods.dev/badge/skills/abysscn/oh-my-dag/omd-research-deep/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.

agentmods 80×15 button for omd-research-deep

Your own site · 80×15
<a href="https://agentmods.dev/skills/abysscn/oh-my-dag/omd-research-deep"><img src="https://agentmods.dev/badge/skills/abysscn/oh-my-dag/omd-research-deep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 120 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,054 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00120 $0.01054
Opus 5.5 $0.00048 $0.00422
Sonnet 5.5 $0.00024 $0.00211
Haiku 4.5 $0.00012 $0.00105

Measured 26d ago against content hash fbdaf6bea208, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

omd-research-deep 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 26d 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.

client-skills/omd-research-deep/SKILL.md · 48 lines

What it actually says

/omd-research-deep — 终极档深度调研

调 omd MCP dag_research(可能带 mcp__omd__ 前缀;未加载先 ToolSearch "dag_research"), 开 super: true + rounds: 3。管线全在引擎内跑完才返回,引擎计数,不问模型"够了吗":

  1. 种子作者化(super 开的就是这个)— 模型把问题拆 3-4 个互补角度 query (机制 / 实践 / 反面 / 生态),各自独立检索并入语料。
  2. council 分解 — conductor 按全部语料 author 领域专家 lens → L×V fanout → per-lens 判优 → 综合 → judge panel → graft。(council 默认就是开的,不用传。)
  3. 多轮缺口补挖(rounds,上限 4)— 轮间做 [模型缺口分析 + 确定性 probe: 引用集 − 已抓集 的缺料补抓],无新增即提前停;二轮起 challenger lens 只挖缺口不重答原题。

用法

dag_research(question: "<研究问题>", super: true, rounds: 3)
  • 异步返回(spawn detached 子进程):立即回 runId,用 dag_status 轮询到完成再取报告(真源 src/mcp/tools/research.ts 的 detached 分支)。
  • summary 进对话;全文在 reportPath(lens 冠军 + 逐轮缺口留痕 + 全部语料附录零丢失)。 关键决策必须 Read 那个文件,别只看 summary。
  • 转述纪律:结论 + 来源 URL + 哪轮缺口补出了什么;「语料未覆盖」的部分如实说。
  • 成本形状:种子×检索 + council + ≤N 轮,比默认档 dag_research 贵数倍 ——真要挖透的领域才用。

前置与降级

  • 必须有 search provider:TAVILY_API_KEY / ANYSEARCH_API_KEY / SEARXNG_URL 任一;OMD_WEB_ORDER=searxng,anysearch,tavily 覆盖默认 failover 序(默认 tavily→anysearch→searxng)。 没有 → 工具响亮拒绝(不会静默降级成"看起来像调研的一段话")。这是有意的: 没有 web 就没有调研。
  • query 带「知乎/zhihu」→ 自动加一路 MediaCrawler 知乎站内搜(需 MEDIACRAWLER_URL + 登录 cookie):分钟级,只用原 query 搜一次;挂了 warn 不断链。
  • 锚点文件 / 显式种子 query 不在 MCP 面上:--anchor(把已有设计笔记原样送进 groundTruth 之首)和 --queries(给死种子、不作者化)只有源码档的 bun run scripts/dag-research.ts 有。要这两个 → 直接跑脚本(需 omd 源码/包目录), 或把锚点要点写进 question 正文。

与既有能力的边界

  • dag_research 默认档(不开 super)= 单问题综合,便宜得多 —— 大多数调研用它就够。
  • omd_web = 只抓不综合 · omd_distill = 吃已有料蒸洞察 · dag-map = codegraph 代码理解。
  • 轻量单点查证走客户端自带的 web 检索,别为一个事实起这条管线。
  • 领域法定源 RAG(会计/法条类)不在此管线。
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. 26d ago Changed · +1 lines · +3 tokens per session fbdaf6bea208
  2. 2mo ago First seen · 47 lines · 117 tokens per session scan A f8e292788ada

Subscribe to this mod's changes

omd-research-deep is a skill published in the GitHub repository AbyssCN/oh-my-dag (44 stars, last pushed 26d ago), licensed MIT. It adds 120 tokens to every session and 1,054 once invoked, about $0.0005 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.

LLMResearchagentic-aiai-agentbuncoding-agentmcp
Related

omd-research-deep compared with skills from other repositories

omd-research-deep next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
omd-research-deepAbyssCN/oh-my-dagSkill4426d agoMITA120Claude Code, Codex
release-notesno-human-ai/no_humanSkill32028d agoMITA41Claude Code
review-this-branchno-human-ai/no_humanSkill32028d agoMITA53Claude Code
file-a-taskno-human-ai/no_humanSkill32028d agoMITA37Claude Code
design-skillOverbrilliant/ob-1Skill2911d agoApache-2.0A82Claude Code, Codex
skill-testandrefetch/postalSkill183yesterdayGPL-3.0A13Claude Code, Codex
interview-and-create-plandoeixd/opencode-ralph-rlmSkill324mo agononeA89Claude Code, Codex