Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/robertbagge/claude-sagan-pluginnpx agentmods add skills/robertbagge/claude-sagan-plugin/deep-researchWrote 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/robertbagge/claude-sagan-plugin/deep-research)<a href="https://agentmods.dev/skills/robertbagge/claude-sagan-plugin/deep-research"><img src="https://agentmods.dev/badge/skills/robertbagge/claude-sagan-plugin/deep-research/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/robertbagge/claude-sagan-plugin/deep-research"><img src="https://agentmods.dev/badge/skills/robertbagge/claude-sagan-plugin/deep-research.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 | $0.00041 | $0.03191 |
| Opus 5 | $0.00020 | $0.01596 |
| Sonnet 5 | $0.00008 | $0.00638 |
| Haiku 4.5 | $0.00004 | $0.00319 |
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 8d 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
deep-research
Execute one round of deep research from a brief produced by /create-brief.
A research project has this layout:
{output_dir}/
├── brief.md # written by /create-brief
├── research/ # one file per topic
│ └── {topic-slug}.md
└── synthesis/ # syntheses across the corpus
├── general.md # the whole-corpus synthesis, rewritten each round
└── {axis-slug}.md # per-axis cuts, owned by /synthesize
Each invocation runs one research round and stages the next:
- Run the next pending round — dispatch one parallel Task agent per missing topic, each writing to
{output_dir}/research/{topic-slug}.md. - Fully rewrite
{output_dir}/synthesis/general.md— incorporating every round executed so far. Tight executive synthesis, not a table of contents in the body. - Propose gap topics for the next round — one-question-at-a-time conversation, then append
## Round N+1to the brief and stop. The user re-invokes to execute the new round.
Why this matters
The user's "better too much research than too little" workflow only works if (a) topic agents run in true parallel — a single message with N tool calls, since sequential calls across messages run serially and waste hours; (b) each topic agent does fresh external web research rather than relying on training data; (c) the synthesis is rewritten each round so it integrates everything rather than accreting fragments; and (d) the brief is append-only so it stays an audit trail of what was researched and when.
Each topic file produced by an agent has this shape:
- 1500–4000 word Markdown file.
- 4–8 H2 sections, structured around the topic's natural axes (no generic template).
- Dense inline citations to external sources, each tagged with its type (
[T1],[T2],[T3]) per the brief's Source types section. - A
## Sourcessection at the bottom with full citation metadata.
The general synthesis has this shape:
- Markdown file, aim for 1500–4000 words — longer is fine when the material warrants it.
- Five H2 sections in this order: Contents (links to brief and topic files), Executive summary, Cross-cutting themes, Tensions and trade-offs, Open questions.
- Executive prose, not a list of facts. Navigation lives in the Contents section so the body can stay synthetic.
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.
- 8d ago First seen · 216 lines · 41 tokens per session scan A 432654678a36
deep-research is a skill published in the GitHub repository robertbagge/claude-sagan-plugin (4 stars, last pushed 22d ago), licensed MIT. It adds 41 tokens to every session and 3,191 once invoked, about $0.0002 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 2026-08-31.
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