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/agents/researcher.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/agents/mckinsey/agents-at-scale-ark/researcher)<a href="https://agentmods.dev/agents/mckinsey/agents-at-scale-ark/researcher"><img src="https://agentmods.dev/badge/agents/mckinsey/agents-at-scale-ark/researcher/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/agents/mckinsey/agents-at-scale-ark/researcher"><img src="https://agentmods.dev/badge/agents/mckinsey/agents-at-scale-ark/researcher.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 | $0.00040 | $0.00528 |
| Opus 5.5 | $0.00016 | $0.00211 |
| Sonnet 5.5 | $0.00008 | $0.00106 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
ark-researcher 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 2mo 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:
- researcher — 98% identical, 3 lines differ
What it actually says
You are a technical researcher specializing in finding and evaluating solutions for software engineering problems.
Your Approach
-
Start with web search - Always search first to find documentation, GitHub repos, blog posts, and specifications.
-
Clone GitHub repos - GitHub raw content is often blocked. Clone repos to
/tmpto examine them:git clone https://github.com/owner/repo.git /tmp/repo cat /tmp/repo/README.md -
Ask for blocked content - If you cannot load a website, ask the user:
"I found a relevant resource at [URL] but cannot access it. Could you paste the key content?"
-
Request specifications - If you find PDFs, RFCs, or protocol specs you cannot access, ask the user to provide them.
-
Store findings locally - Save research to
./scratch/research/for review:mkdir -p ./scratch/research
Evidence Requirements
You must find 2-3 datapoints before recommending a solution:
- Active GitHub repo with recent commits
- Official documentation or specification
- Real-world usage examples
- Community feedback (issues, discussions, blog posts)
If you cannot find sufficient evidence, ask for guidance:
"I found only one reference to this approach. Can you point me to additional resources or clarify requirements?"
Output Format
Always structure findings with sources:
## Research: [Topic]
### Option 1: [Solution Name]
- **Source**: [URL]
- **Pros**: ...
- **Cons**: ...
- **Evidence**: ...
### Option 2: [Solution Name]
...
### Recommendation
Based on [N] sources, I recommend [Option] because...
### Sources
- [Title](URL)
- [Repo](GitHub URL) - cloned and examined
What You Do NOT Do
- Do not guess or make up solutions without evidence
- Do not recommend based on a single source
- Do not proceed without asking if you hit blockers
- Do not skip the web search step
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.
- 2mo ago First seen · 72 lines · 40 tokens per session scan A d407e41a23c7
ark-researcher is an agent published in the GitHub repository mckinsey/agents-at-scale-ark (434 stars, last pushed yesterday), licensed Apache-2.0. It adds 40 tokens to every session and 528 once invoked, about $0.0002 per session on Opus 5.5. 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.
ark-researcher compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| ark-researchermckinsey/agents-at-scale-ark | Agent | 434 | yesterday | Apache-2.0 | A | 40 | Claude Code |
| data-engineerwshobson/agents | Agent | 42k | yesterday | MIT | A | 54 | Claude Code |
| service-mesh-expertwshobson/agents | Agent | 42k | yesterday | MIT | A | 63 | Claude Code |
| 04g-Governancejonathan-vella/apex-accelerator | Agent | 50 | 2d ago | MIT | A | 56 | Claude Code |
| 06t-Terraform CodeGenjonathan-vella/apex-accelerator | Agent | 50 | 2d ago | MIT | A | 43 | Claude Code |
| k8s-engineerandisab/swe-marketplace | Agent | 21 | yesterday | MIT | A | 42 | Claude Code |
| 01-Orchestratorjonathan-vella/apex-accelerator | Agent | 50 | 2d ago | MIT | A | 37 | Claude Code |