Borrowing it
Nothing to install: this file belongs to mckinsey/agents-at-scale-ark. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mckinsey/agents-at-scale-ark/main/.claude/skills/research/SKILL.mdgit clone --depth 1 https://github.com/mckinsey/agents-at-scale-arkWrote 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/mckinsey/agents-at-scale-ark/research)<a href="https://agentmods.dev/skills/mckinsey/agents-at-scale-ark/research"><img src="https://agentmods.dev/badge/skills/mckinsey/agents-at-scale-ark/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/mckinsey/agents-at-scale-ark/research"><img src="https://agentmods.dev/badge/skills/mckinsey/agents-at-scale-ark/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 | $0.00030 | $0.00582 |
| Opus 5 | $0.00015 | $0.00291 |
| Sonnet 5 | $0.00006 | $0.00116 |
| Haiku 4.5 | $0.00003 | $0.00058 |
Grade A, and why
ark-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.
Copies of this mod
1 near-identical copy found in the catalogue:
- research — 86% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ark Research
Research technical solutions and gather evidence before implementation.
Research Process
1. Web Search First
Always start with web search to find:
- Official documentation
- GitHub repositories
- Blog posts and tutorials
- Protocol specifications (PDFs, RFCs)
2. Examine GitHub Repositories
GitHub raw content is often blocked. Clone repos to examine them:
cd /tmp
git clone https://github.com/owner/repo.git
cat /tmp/repo/README.md
Look for:
- README documentation
- Code examples
- Architecture patterns
- Dependencies and requirements
3. Handle Blocked Content
If a website cannot be loaded:
- Ask the user to paste the relevant content
- Request PDFs or specification documents
- Ask for screenshots if visual content is needed
Example prompt:
"I found a relevant resource at [URL] but cannot access it. Could you paste the key content or provide the PDF?"
4. Local Research Workspace
Store findings in ./scratch/research/ for review:
mkdir -p ./scratch/research
Save:
- Cloned repo summaries
- Code snippets
- Architecture diagrams
- Comparison notes
5. Evidence Requirements
Minimum 2-3 datapoints required before recommending a solution:
- GitHub repo with active maintenance
- Documentation or specification
- Real-world usage examples
- Community feedback (issues, discussions)
If insufficient evidence, ask for guidance:
"I found only one reference to this approach. Can you point me to additional resources or clarify the requirements?"
Output Format
Always back up findings with sources:
## Research: [Topic]
### Option 1: [Solution Name]
- **Source**: [URL or repo link]
- **Pros**: ...
- **Cons**: ...
- **Evidence**: [What confirms this works]
### Option 2: [Solution Name]
...
### Recommendation
Based on [N] sources, I recommend [Option] because...
### Sources
- [Title](URL)
- [Repo](GitHub URL) - cloned and examined
- [Spec](URL) - user provided
Example Usage
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 · 105 lines · 30 tokens per session scan A a31294b6dfea
ark-research is a skill published in the GitHub repository mckinsey/agents-at-scale-ark (423 stars, last pushed today), licensed Apache-2.0. It adds 30 tokens to every session and 582 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 2026-09-03.
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