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 runxhq/runx --skill deep-researchgit clone --depth 1 https://github.com/runxhq/runxWrote 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/runxhq/runx/deep-research)<a href="https://agentmods.dev/skills/runxhq/runx/deep-research"><img src="https://agentmods.dev/badge/skills/runxhq/runx/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/runxhq/runx/deep-research"><img src="https://agentmods.dev/badge/skills/runxhq/runx/deep-research.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 53 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 | $0.00023 | $0.00796 |
| Opus 5 | $0.00012 | $0.00398 |
| Sonnet 5 | $0.00005 | $0.00159 |
| Haiku 4.5 | $0.00002 | $0.00080 |
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 6d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Turn one important question into a durable operator brief: what the answer is, what evidence supports it, what remains uncertain, and what posture the reader should take next. Use this when a quick answer is too shallow but an open-ended report would obscure the decision.
The output should feel like a thoughtful memo, not a narration of the research process. It leads with the decision and operational implication, then exposes the evidence, inference, alternatives, and unresolved questions that justify that posture.
Composes
ghostwrite#draftresearch#local-filesresearch#research
When to use it
Use deep-research for architecture choices, market or ecosystem questions,
product bets, trust decisions, or other consequential analysis that needs a
reader-ready synthesis. Use plain research when the evidence packet itself is
the desired artifact. Use content-pipeline when the primary outcome is public
content for a known channel.
Do not use this skill for an unbounded literature review, a daily trend recap, or research whose sources have not been fetched through a governed reader.
How the chain works
- The caller supplies the exact question and audience plus either governed
source packets or bounded local paths. The
local-filesrunner reads those paths through Runx's native filesystem boundary; it does not tunnel local evidence through HTTP. - The canonical
researchskill admits and indexes those sources, separates evidence from inference, and verifies every citation and recommendation. - Only a ready research packet proceeds to
ghostwrite. The writing stage turns decision support into a clear brief without introducing unsupported facts. - The final artifact preserves the research and content packet bindings. It remains local and is not a publication claim.
Local research and drafting need no approval because they do not cross an external boundary. If the brief is later sent, posted, or published, the provider delivery skill owns approval, idempotency, acknowledgement, and readback.
What ships with it
1 file 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.
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.
- 6d ago First seen · 89 lines · 23 tokens per session scan A 90770d1d99fe
deep-research is a skill published in the GitHub repository runxhq/runx (84 stars, last pushed yesterday), licensed Apache-2.0. It adds 23 tokens to every session and 796 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 2026-09-03.
Other skills, from other repositories
dd-risk-scoring
Calculate weighted risk scores from specialist agent reports. Use when synthesizing multiple due diligence reports into an overall investment risk assessment.
dd-synthesis
Synthesize findings from multiple specialist agents into a coherent investment recommendation. Use when combining reports from 9+ specialist agents into a single decision document.
dd-report-format
Standard output format for due diligence analysis reports. Use when producing your final analysis to ensure consistency across all specialist agents.
dd-sourcing
Document sources properly for due diligence reports. Use when citing any external data, ensuring full traceability and auditability of findings.
dd-verification
Verify claims in pitch decks and founder statements using multiple independent sources. Use when analyzing any claim about market size, traction, team background, or competitive positioning.
codew-release-qa-sweep
Use before claiming Codewhale release work is done: run the full gate sweep and list the manual QA targets.