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/commands/googilyboogily/googilyboogily-claude-power-tools/research)<a href="https://agentmods.dev/commands/googilyboogily/googilyboogily-claude-power-tools/research"><img src="https://agentmods.dev/badge/commands/googilyboogily/googilyboogily-claude-power-tools/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/commands/googilyboogily/googilyboogily-claude-power-tools/research"><img src="https://agentmods.dev/badge/commands/googilyboogily/googilyboogily-claude-power-tools/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 | $0.00009 | $0.01899 |
| Opus 5 | $0.00005 | $0.00949 |
| Sonnet 5 | $0.00002 | $0.00380 |
| Haiku 4.5 | $0.00001 | $0.00190 |
Grade A, and why
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 9d 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🔬 Research Command
Conduct deep, parallel research on any topic using multiple specialized subagents.
Research Query
$ARGUMENTS
Research Process
Phase 1: Query Classification (CRITICAL FIRST STEP)
PRIMARY DECISION: Classify the query type to determine research strategy
Query Types:
-
BREADTH-FIRST QUERIES (Wide exploration)
- Characteristics: Multiple independent aspects, survey questions, comparisons
- Examples: "Compare all major cloud providers", "List board members of S&P 500 tech companies"
- Strategy: 5-10 parallel subagents, each exploring different aspects
- Each subagent gets narrow, specific tasks
-
DEPTH-FIRST QUERIES (Deep investigation)
- Characteristics: Single topic requiring thorough understanding, technical deep-dives
- Examples: "How does transformer architecture work?", "Explain quantum entanglement"
- Strategy: 2-4 subagents with overlapping but complementary angles
- Each subagent explores the same topic from different perspectives
-
SIMPLE FACTUAL QUERIES (Quick lookup)
- Characteristics: Single fact, recent event, specific data point
- Examples: "When was GPT-4 released?", "Current CEO of Microsoft"
- Strategy: 1-2 subagents for verification
- Focus on authoritative sources
After Classification, Determine:
- Resource Allocation: Based on query type (1-10 subagents)
- Search Domains: Academic, technical, news, or general web
- Depth vs Coverage: How deep vs how wide to search
Phase 2: Parallel Research Execution
Based on the query classification, spawn appropriate research subagents IN A SINGLE MESSAGE for true parallelization.
CRITICAL: Parallel Execution Pattern Use multiple Task tool invocations in ONE message, ALL with subagent_type="research-expert".
MANDATORY: Start Each Task Prompt with Mode Indicator You MUST begin each task prompt with one of these trigger phrases to control subagent behavior:
- Quick Verification (3-5 searches): Start with "Quick check:", "Verify:", or "Confirm:"
- Focused Investigation (5-10 searches): Start with "Investigate:", "Explore:", or "Find details about:"
- Deep Research (10-15 searches): Start with "Deep dive:", "Comprehensive:", "Thorough research:", or "Exhaustive:"
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.
- 9d ago First seen · 199 lines · 9 tokens per session scan A e9a02b5fbf58
research is a command published in the GitHub repository GoogilyBoogily/googilyboogily-claude-power-tools (2 stars, last pushed 4mo ago), licensed MIT. It adds 9 tokens to every session and 1,899 once invoked, about $0.0000 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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context
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spec-research
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triage
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land
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sync
Manually trigger plan-sync to update downstream task specs after implementation drift.
slack-gif-creator
Toolkit for creating animated GIFs optimized for Slack, with validators for size constraints and composable animation primitives. This skill applies when users request animated GIFs or emoji animations for Slack from descriptions like "make me a GIF for Slack of X doing Y".