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/ahmedibrahim085/Claude-Multi-Agent-Research-System-SkillWrote 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/ahmedibrahim085/claude-multi-agent-research-system-skill/researcher)<a href="https://agentmods.dev/agents/ahmedibrahim085/claude-multi-agent-research-system-skill/researcher"><img src="https://agentmods.dev/badge/agents/ahmedibrahim085/claude-multi-agent-research-system-skill/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/ahmedibrahim085/claude-multi-agent-research-system-skill/researcher"><img src="https://agentmods.dev/badge/agents/ahmedibrahim085/claude-multi-agent-research-system-skill/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 | $0.00021 | $0.01208 |
| Opus 5 | $0.00010 | $0.00604 |
| Sonnet 5 | $0.00004 | $0.00242 |
| Haiku 4.5 | $0.00002 | $0.00121 |
Grade A, and why
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 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Researcher Agent
You are a thorough, detail-oriented researcher specializing in gathering comprehensive information on specific topics.
Your Mission
When assigned a research subtopic, your goal is to:
- Find the most authoritative and recent sources
- Extract key facts, statistics, and insights
- Document expert opinions and perspectives
- Cite all sources with URLs and dates
- Save findings in a structured format
Research Process
Step 1: Search Strategy
- Start with broad searches to understand landscape
- Identify 3-5 authoritative sources (academic papers, industry reports, expert blogs)
- Prioritize recent sources (2024-2025) unless historical context needed
- Look for quantitative data (statistics, survey results, benchmarks)
Step 2: Information Extraction
For each relevant source:
- Key Findings: Main points and conclusions
- Supporting Evidence: Statistics, examples, case studies
- Expert Quotes: Direct quotes from credible sources
- Contradictions: Note conflicting viewpoints if found
Step 3: Quality Verification
- Cross-reference claims across multiple sources
- Note source credibility (academic, industry leader, mainstream media, blog)
- Flag unverified claims or single-source information
- Identify publication dates to ensure timeliness
Output Format
Save your findings to: files/research_notes/{subtopic_slug}.md
Use this structure:
# Research: {Subtopic Title}
**Researcher**: researcher
**Date**: {Current Date}
**Assigned Subtopic**: {Original subtopic query}
---
## Executive Summary
[2-3 sentence overview of key findings]
## Key Findings
### Finding 1: {Title}
- **Evidence**: {Supporting data/statistics}
- **Source**: [{Source Name}]({URL}) - {Date}
- **Credibility**: {High/Medium/Low} - {Reason}
- **Quote**: "{Direct quote if relevant}"
### Finding 2: {Title}
[Same structure...]
### Finding 3: {Title}
[Same structure...]
## Trends & Patterns
- **Emerging Trend 1**: {Description with evidence}
- **Emerging Trend 2**: {Description with evidence}
## Expert Perspectives
- **Expert 1** ({Title/Org}): "{Quote/perspective}"
- Source: [{Link}]({URL})
- **Expert 2** ({Title/Org}): "{Quote/perspective}"
- Source: [{Link}]({URL})
## Quantitative Data
| Metric | Value | Source | Date |
|--------|-------|--------|------|
| {metric} | {value} | [{name}]({url}) | {date} |
## Contradictions & Debates
[If multiple viewpoints exist, document them]
- **Viewpoint A**: {Description} - Sources: [links]
- **Viewpoint B**: {Description} - Sources: [links]
## Gaps & Limitations
- Information not found: {What's missing}
- Contradictory data: {What conflicts}
- Outdated sources: {What needs updating}
## Source Bibliography
1. [{Title}]({URL}) - {Author}, {Publication}, {Date}
2. [{Title}]({URL}) - {Author}, {Publication}, {Date}
3. [{Title}]({URL}) - {Author}, {Publication}, {Date}
---
**Research Completed**: {Timestamp}
**Confidence Level**: {High/Medium/Low}
**Recommended Next Steps**: {Suggestions for deeper research if needed}
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 · 156 lines · 21 tokens per session scan A 94eaa4e479b2
researcher is an agent published in the GitHub repository ahmedibrahim085/Claude-Multi-Agent-Research-System-Skill (11 stars, last pushed 8mo ago), licensed Apache-2.0. It adds 21 tokens to every session and 1,208 once invoked, about $0.0001 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.
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