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 malob/nix-config --skill deep-research-teamgit clone --depth 1 https://github.com/malob/nix-configWrote 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/malob/nix-config/deep-research-team)<a href="https://agentmods.dev/skills/malob/nix-config/deep-research-team"><img src="https://agentmods.dev/badge/skills/malob/nix-config/deep-research-team/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/malob/nix-config/deep-research-team"><img src="https://agentmods.dev/badge/skills/malob/nix-config/deep-research-team.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 199 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00132 | $0.05298 |
| Opus 5 | $0.00066 | $0.02649 |
| Sonnet 5 | $0.00026 | $0.01060 |
| Haiku 4.5 | $0.00013 | $0.00530 |
Grade A, and why
deep-research-team 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.
How it starts
The opening of the file, as written. The whole thing — 561 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research Team (Lead Orchestrator)
Conduct thorough, iterative research by coordinating a persistent team of researcher agents across multiple rounds. This architecture enables mid-investigation steering, targeted follow-up based on emerging findings, and cross-agent verification.
Architecture Overview
Round 1: Investigation Round 2: Follow-up Synthesis
┌──────────┐ ┌──────────┐ ┌────────┐
│Researcher│ sends findings │Researcher│ sends findings │ │
│ A ├─────────┬───────>│ A ├─────────┬───────>│ │
└──────────┘ │ └──────────┘ │ │ │
│ │ │ │
v dispatches v │ │
┌──────────┐ ┌────────┐ ┌──────────┐ ┌────────┐ │ Lead │
│Researcher├───>│ Lead │───>│Researcher├───>│ Lead │───>│ synth │
│ B │ │triages │ │ B │ │triages │ │ esizes│
└──────────┘ └────────┘ └──────────┘ └────────┘ │ │
^ ^ │ │
┌──────────┐ │ ┌──────────┐ │ │ │
│Researcher├─────────┴───────>│Researcher├─────────┴───────>│ │
│ C │ sends findings │ C │ sends findings │ │
└──────────┘ └──────────┘ └────────┘
Key principles:
-
No peer-to-peer researcher communication. All coordination goes through the lead. This preserves the independence that accounts for 87% of multi-agent gains (Choi et al.) and avoids sycophancy failures (Wynn et al.). Researchers never see each other's findings.
-
Multi-round iteration. The lead triages Round 1 findings and creates targeted Round 2 tasks for gaps, conflicts, and promising leads.
-
Cross-agent verification (Comprehensive scope). The lead asks Researcher A to verify Researcher B's high-impact single-source claim. The verifier only sees the claim and its source, not the original researcher's full analysis.
What ships with it
25 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- dev/ITERATION-LOG.md 24 KB
- dev/iterations/iteration-01.md 7.8 KB
- dev/iterations/iteration-02.md 10 KB
- dev/iterations/iteration-03.md 8.8 KB
- dev/iterations/iteration-04.md 6.4 KB
- dev/iterations/iteration-05.md 3.6 KB
- dev/iterations/iteration-06.md 6.9 KB
- dev/iterations/iteration-07.md 6.7 KB
- dev/iterations/iteration-08.md 4.1 KB
- dev/iterations/iteration-09.md 7.1 KB
- dev/iterations/iteration-10.md 3.8 KB
- dev/iterations/iteration-11.md 6.9 KB
- dev/iterations/iteration-12.md 6.3 KB
- dev/iterations/iteration-13.md 6.8 KB
- dev/RESEARCH.md 13 KB
- references/question-types.md 5.3 KB
- references/researcher-prompt.md 13 KB
- references/templates/consumer.md 916 B
- references/templates/contested.md 994 B
- references/templates/emerging-frontier.md 899 B
- references/templates/factual.md 752 B
- references/templates/opinion-sentiment.md 893 B
- references/templates/scientific-health.md 1012 B
- references/templates/technical.md 942 B
- scripts/analyze-transcripts.py 15 KB runs code
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 · 561 lines · 132 tokens per session scan A 7ece3f8fdb89
deep-research-team is a skill published in the GitHub repository malob/nix-config (462 stars, last pushed 2d ago), licensed MIT. It adds 132 tokens to every session and 5,298 once invoked, about $0.0007 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.
Other skills, from other repositories
restart__pull_request
A pull-request restart workflow for replacing a messy review with a new pull request containing one clean commit and a summary of the earlier discussion.
validate__japanese
A Japanese-language review for Markdown documents, such as READMEs, manuals, and blog drafts. It checks writing style, spacing, line breaks, and links to real code symbols.
write__structured_comment
A code-commenting rule for recording only knowledge that cannot be expressed in the code, such as unfinished facts, outside-world facts, or explicit user instructions. Each comment must use an approved marker such as TODO or FIXME and stay short.
clean__comment_out
A source-cleanup workflow that removes non-documentation comments from files changed during coding, except for approved markers such as TODO, FIXME, or SAFETY.
implement__feature
A three-phase workflow for carrying out implementation tasks. It plans and tests the work, delegates implementation using test-driven development (TDD), then runs repeated code reviews.
prepare__issue
An orchestration workflow that prepares an already acknowledged issue for implementation and changes its status to ready when finished.