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 skills add kimsb2429/claude-skills --skill deep-divegit clone --depth 1 https://github.com/kimsb2429/claude-skillsWrote 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.
[](https://agentmods.dev/skills/kimsb2429/claude-skills/deep-dive)<a href="https://agentmods.dev/skills/kimsb2429/claude-skills/deep-dive"><img src="https://agentmods.dev/badge/skills/kimsb2429/claude-skills/deep-dive/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.
<a href="https://agentmods.dev/skills/kimsb2429/claude-skills/deep-dive"><img src="https://agentmods.dev/badge/skills/kimsb2429/claude-skills/deep-dive.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.01381 |
| Opus 5 | $0.00015 | $0.00691 |
| Sonnet 5 | $0.00006 | $0.00276 |
| Haiku 4.5 | $0.00003 | $0.00138 |
Grade A, and why
deep-dive 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 9d 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.
This is a copy
94% identical to deep-dive — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Dive
Autonomous deep research using the same DAG-based planning pattern as Google's Deep Research — but running entirely on Claude Code with no external dependencies.
How it works
- Plan — decompose the question into a DAG of sub-questions with dependencies
- Fan out — run independent sub-questions in parallel via Agent subagents
- Gap analysis — each subagent returns findings + identified gaps
- Iterate — gaps become new sub-questions, fed back into the DAG
- Synthesize — once all nodes complete, produce a final report
Steps
1. Decompose into a DAG
Given the research question, generate a DAG of sub-questions. Each node has:
- id: short identifier (e.g.,
q1,q2a) - question: the specific sub-question to research
- depends_on: list of node IDs whose answers are needed first (empty = no dependencies)
Rules for decomposition:
- Start with foundational/context-setting questions that have no dependencies
- Build toward analytical/comparative questions that depend on foundational answers
- Aim for 4-8 nodes. If the topic needs more, cap at 12.
- Each node should be answerable with 1-3 web searches
- Questions should be specific enough that a researcher with no other context can answer them
Print the DAG as a table so the first brain can see the plan, then immediately proceed to execution — do not wait for confirmation.
Create a task for each DAG node using TaskCreate (description: the sub-question, status: pending). Also create tasks for "Gap analysis" and "Synthesize report". Update each task to in_progress when its wave launches and completed when the subagent returns. This gives the first brain real-time visibility into progress.
| ID | Question | Depends on |
|----|----------|------------|
| q1 | ... | — |
| q2 | ... | — |
| q3 | ... | q1 |
| q4 | ... | q1, q2 |
2. Execute in dependency order
Process the DAG in topological order:
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.
- 9d ago First seen · 138 lines · 29 tokens per session scan A 6b69623dfd1e
deep-dive is a skill published in the GitHub repository kimsb2429/claude-skills (1 stars, last pushed 4mo ago), licensed MIT. It adds 29 tokens to every session and 1,381 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to deep-dive, differing in 4 lines, and is treated as a copy.
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