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 staruhub/ClaudeSkills --skill geek-skills-deep-researchgit clone --depth 1 https://github.com/staruhub/ClaudeSkillsWrote 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/staruhub/claudeskills/geek-skills-deep-research)<a href="https://agentmods.dev/skills/staruhub/claudeskills/geek-skills-deep-research"><img src="https://agentmods.dev/badge/skills/staruhub/claudeskills/geek-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/staruhub/claudeskills/geek-skills-deep-research"><img src="https://agentmods.dev/badge/skills/staruhub/claudeskills/geek-skills-deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00172 | $0.02461 |
| Opus 5 | $0.00086 | $0.01230 |
| Sonnet 5 | $0.00034 | $0.00492 |
| Haiku 4.5 | $0.00017 | $0.00246 |
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 — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research V8.1
This skill is for evidence-rich research outputs, not for every question that happens to mention “analysis”.
The V8 shift is simple:
- Single-agent first. Start with one lead agent and only fan out when parallel work will clearly help.
- Thin harness, fat skill. Put reusable judgment and workflow here; keep deterministic checks in scripts.
- Context organization over prompt stuffing. Load the minimum active context bundle, then pull in references only when needed.
- Eval and observability built in. A good report is not enough; the run must also be diagnosable and improvable.
What this skill should produce
Choose the lightest artifact that satisfies the task.
| Output type | Use when | Typical length | Required artifacts |
|---|---|---|---|
| Brief memo | user wants a concise answer with evidence | 800-1800 words | research-plan.md, registry.md, draft.md, run-summary.json |
| Full report | user asks for comprehensive analysis / literature review / decision document | 2500-6000 words | all core artifacts + evaluation.md |
| Delta update | user says “continue”, “second round”, “what changed”, “deepen round 2” | 600-1800 words | prior round handoff (references/handoff-format.md) + new notes + delta draft |
If the user did not ask for a long report, default to Brief memo.
When NOT to use this skill
Do not activate for:
- quick fact lookups or simple definitions
- summarizing a single provided article/PDF/page
- short comparisons the model can answer directly from 1-2 sources
- brainstorming without evidence requirements
- tasks where the user explicitly wants a short answer, not a report
If in doubt, ask yourself: Does this task need a reusable evidence artifact and multi-source synthesis? If not, do something simpler.
Org-policy boundary
This skill does not replace system policies, enterprise guardrails, or repo-level instructions. Put these outside the skill:
- data handling / PII / compliance rules
- approval requirements for external access or irreversible actions
- org-wide style and review policy
- environment-specific permissions
What ships with it
16 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.
- assets/report_template.md 1006 B
- evals/routing-evals.json 3.8 KB
- evals/runbook.md 967 B
- references/evaluator-prompt.md 4.0 KB
- references/handoff-format.md 3.9 KB
- references/landscape-scan.md 1.2 KB
- references/methodology.md 5.7 KB
- references/observability.md 3.0 KB
- references/quality-gates.md 5.5 KB
- references/report-assembly.md 3.4 KB
- references/research-notes-format.md 2.2 KB
- references/subagent-prompt.md 1.7 KB
- references/tension-discovery.md 1.2 KB
- scripts/emit_run_summary.py 5.0 KB runs code
- scripts/source_evaluator.py 12 KB runs code
- scripts/verify_citations.py 11 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.
- 12d ago First seen · 252 lines · 172 tokens per session scan A 6fa959457927
deep-research is a skill published in the GitHub repository staruhub/ClaudeSkills (712 stars, last pushed 1mo ago), licensed MIT. It adds 172 tokens to every session and 2,461 once invoked, about $0.0009 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
report-helper
A Chinese-language research workflow that searches the internet and produces a formatted PDF report about a specified topic.
giasip-dispatch
A skill for sending a task to other AI models and collecting their results. It supports direct API calls, command-line tools and built-in internal agents, depending on the model.
searchcans-deep-research
Conduct bounded, evidence-led, account-aware web research with SearchCans SERP API and Reader API. Use for cited-source research that needs current localized web evidence, such as market, competitor, technology, policy, company, or product research; plan 3–5 subquestions, set a source budget, read selected pages…
searchcans-serp-content-gap
Analyze a current, geo-targeted Google or Bing SERP with SearchCans and turn observed result features, People Also Ask questions, related searches, knowledge graph, and news signals into an evidence-backed, account-aware content decision brief. Use for localized SEO/GEO planning, keyword research, competitor-page…
searchcans-market-watch
Build a current, geo-targeted market-watch snapshot from Google Search, Google News, Bing Search, and selected Reader extracts. Use for competitor and category monitoring, PR/news tracking, launch intelligence, and URL-level change checks between two bounded runs; reject malformed, placeholder, or un-attributable News…
searchcans-product-serp-brief
Create a localized product-search evidence brief from Google Shopping, Google web results, Google Images, and optional Reader extracts of explicit merchant URLs. Use for e-commerce category research, competitor assortment discovery, product-page planning, and market-specific merchandising briefs.