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 ChuckSRQ/awesome-hermes-skills --skill deep-researchgit clone --depth 1 https://github.com/ChuckSRQ/awesome-hermes-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/chucksrq/awesome-hermes-skills/deep-research)<a href="https://agentmods.dev/skills/chucksrq/awesome-hermes-skills/deep-research"><img src="https://agentmods.dev/badge/skills/chucksrq/awesome-hermes-skills/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/chucksrq/awesome-hermes-skills/deep-research"><img src="https://agentmods.dev/badge/skills/chucksrq/awesome-hermes-skills/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 | $0.00063 | $0.02017 |
| Opus 5 | $0.00032 | $0.01009 |
| Sonnet 5 | $0.00013 | $0.00403 |
| Haiku 4.5 | $0.00006 | $0.00202 |
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 12d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
A local research engine that takes ONE question and produces multi-angle analysis no single Google search or prompt could match.
Knowledge base: ~/research-skill-graph/
Invocations: say "do deep research on [your question]" or "/skill deep-research" then ask your question
How It Works
The system forces structured thinking through 9 research lenses, each rethinking the question from a fundamentally different angle. Lenses are defined in the skill graph folder and evolve over time.
The 9 Lenses (in execution order):
- technical — mechanics, data, hard numbers. Strip away narrative.
- economic — money flows, incentives, cost structures, who pays/profits.
- historical — patterns, precedent, what failed before.
- business — competitive landscape, unit economics, who's winning/losing.
- strategic — key moves, leverage points, game theory. What matters in 3-10 years.
- customer — real buyer vs. user, JTBD, trust signals, purchase blockers.
- product — capabilities, limits, failure modes, MVPs.
- contrarian — stress-test the consensus. Who benefits from the current narrative?
- first-principles — rebuild from ground truth. Forget assumptions.
Execution Protocol
Execution Protocol
When you receive a research question:
Step 1: Read the command center at ~/research-skill-graph/index.md — it contains the full briefing template and node map.
Step 2: Read methodology/research-frameworks.md to pick the right approach for the question type:
- "Is X true?" → Verification framework
- "Why is X happening?" → Causal analysis framework
- "What happens if X?" → Scenario planning framework
- "What should I do about X?" → Decision support framework
Step 3: Read methodology/source-evaluation.md — apply the 5-tier trust system to every source:
- Tier 1: Primary data (raw datasets, peer-reviewed studies)
- Tier 2: Expert analysis (research institutions, long-form journalism)
- Tier 3: Informed commentary (expert blogs, think tank reports)
- Tier 4: General media (major news, Wikipedia — verify upstream)
- Tier 5: Social/anecdotal (Twitter, Reddit — signal detection only)
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.
- 12d ago First seen · 165 lines · 63 tokens per session scan A ac1fbe184748
deep-research is a skill published in the GitHub repository ChuckSRQ/awesome-hermes-skills (76 stars, last pushed 4mo ago), licensed MIT. It adds 63 tokens to every session and 2,017 once invoked, about $0.0003 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
expert-research
Expert-mode deep analysis for ambiguous or high-stakes topics. Use when the task needs rigorous evidence, tradeoff analysis, and recommendation confidence. The workflow either performs deep research with available sources/tools or asks the user for targeted inputs needed to complete a high-quality analysis.
Deep Research
Produce a deep, structured research report on a topic: decompose into key dimensions, analyze each with evidence and reasoning, synthesize cross-cutting insights, and surface open questions. Use for deep research, analysis, and literature/landscape reviews.
research-ops
Use when someone hands you an open question whose answer must survive scrutiny — a tech choice, a regulation, a what-is-actually-true-about-X — with every non-obvious claim dated and sourced, source disagreements surfaced rather than averaged away, and a cited memo as the deliverable. Also for refreshing a stale…
paper-workbench
Researcher-profile-driven paper intake and literature workbench for academic workflows. Use whenever the user wants to skim, deep-read, card, compare, synthesize, map research gaps, or build a literature review from papers, arXiv/AlphaXiv links, DOIs, PDFs, or existing workbench artifacts. Trigger even when the user…
research-synthesis
Web research and synthesis using agy CLI.
Deep Research Synthesizer
Optional reference for source-grounded synthesis with uncertainty and retrieval limits.