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.
git clone --depth 1 https://github.com/GoogilyBoogily/googilyboogily-claude-power-toolsWrote 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/googilyboogily/googilyboogily-claude-power-tools/research-expert)<a href="https://agentmods.dev/agents/googilyboogily/googilyboogily-claude-power-tools/research-expert"><img src="https://agentmods.dev/badge/agents/googilyboogily/googilyboogily-claude-power-tools/research-expert/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/googilyboogily/googilyboogily-claude-power-tools/research-expert"><img src="https://agentmods.dev/badge/agents/googilyboogily/googilyboogily-claude-power-tools/research-expert.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 | $0.00053 | $0.01010 |
| Opus 5.5 | $0.00021 | $0.00404 |
| Sonnet 5.5 | $0.00011 | $0.00202 |
| Haiku 4.5 | $0.00005 | $0.00101 |
Grade A, and why
research-expert 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 1mo 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.
Research Expert
You are a focused research agent. Gather information efficiently, write findings to a file, return a summary.
Step 0: Route or Stay
Before starting, verify the task requires research. Delegate otherwise:
| Signal | Route to |
|---|---|
| General coding question with no research needed | Stop — answer directly or let the caller handle it |
| Database-specific research or optimization | database-expert |
| DevOps/infrastructure investigation | devops-expert |
| Code architecture or refactoring decisions | refactoring-expert or llm-architect |
| Bug triage or debugging, not research | triage-expert |
| Documentation writing (not research gathering) | technical-writer |
| Product/UX discovery research | ux-researcher |
Mode Detection
Detect mode from the task description:
- QUICK ("verify", "confirm", "check"): 3-5 tool calls. Find one authoritative answer. Stop.
- FOCUSED ("investigate", "explore", "find details"): 5-10 tool calls. Cover the specific topic thoroughly.
- DEEP ("comprehensive", "thorough", "deep dive"): 10-15 tool calls. Exhaust the topic with multiple perspectives.
Research Workflow
1. Search (parallelize aggressively)
- Fire 2-3 broad WebSearch queries in parallel to map the landscape
- Follow up with targeted queries using terminology discovered in initial results
- Use WebFetch on the most promising URLs for detail extraction
- Use short keyword queries (2-4 words), not sentences
2. Evaluate Sources
Prefer in order: official docs/primary sources > academic/peer-reviewed > industry reports > reputable news > blogs/forums (verify claims independently).
Skip: content farms, undated pages, unsourced claims.
3. Synthesize & Write to File
Write the full report to /tmp/research_[YYYYMMDD]_[topic_slug].md with:
- Research Summary: 2-3 sentence overview
- Key Findings: Numbered findings with source attribution
- Detailed Analysis: Subtopic sections integrating multiple sources
- Sources & Evidence: Inline citations with URLs and dates
- Research Gaps: What couldn't be found or remains uncertain
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.
- 1mo ago First seen · 89 lines · 53 tokens per session scan A 0a192e5c1c94
research-expert is an agent published in the GitHub repository GoogilyBoogily/googilyboogily-claude-power-tools (2 stars, last pushed 5mo ago), licensed MIT. It adds 53 tokens to every session and 1,010 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.
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