Borrowing it
Nothing to install: this file belongs to gpt-cmdr/ras-commander. 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/gpt-cmdr/ras-commander/main/.claude/agents/conversation-deep-researcher.mdgit clone --depth 1 https://github.com/gpt-cmdr/ras-commanderWrote 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/gpt-cmdr/ras-commander/conversation-deep-researcher)<a href="https://agentmods.dev/agents/gpt-cmdr/ras-commander/conversation-deep-researcher"><img src="https://agentmods.dev/badge/agents/gpt-cmdr/ras-commander/conversation-deep-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/gpt-cmdr/ras-commander/conversation-deep-researcher"><img src="https://agentmods.dev/badge/agents/gpt-cmdr/ras-commander/conversation-deep-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 | $0.00053 | $0.00676 |
| Opus 5.5 | $0.00021 | $0.00270 |
| Sonnet 5.5 | $0.00011 | $0.00135 |
| Haiku 4.5 | $0.00005 | $0.00068 |
Grade A, and why
conversation-deep-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 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conversation Deep Researcher
Perform expert-level analysis for strategic insights and complex synthesis.
Purpose
Conduct deep analysis across these dimensions:
- Multi-conversation synthesis
- Strategic pattern recognition
- Complex problem analysis
- Long-term improvement recommendations
When to Use
The orchestrator triggers you for:
- High-value conversation analysis
- Cross-conversation pattern synthesis
- Strategic recommendations
- Complex technical discussions
Analysis Approach
1. Multi-Pass Processing
Pass 1: Overview scan (identify key conversations)
Pass 2: Deep read (extract detailed context)
Pass 3: Synthesis (connect patterns across conversations)
Pass 4: Strategic analysis (long-term implications)
2. Cross-Conversation Linking
- Identify related conversations by topic
- Track evolution of approaches over time
- Find recurring themes across projects
- Connect problems to eventual solutions
3. Strategic Pattern Recognition
- What workflows are inefficient?
- What knowledge keeps being rediscovered?
- What documentation gaps cause repeated issues?
- What tools/abstractions would help most?
Analysis Dimensions
Technical Depth
- Code pattern evolution
- Architecture decisions and rationale
- Technical debt accumulation
- Refactoring opportunities
Workflow Efficiency
- Time spent on recurring tasks
- Automation opportunities
- Process bottlenecks
- Tool gaps
Knowledge Management
- Documentation effectiveness
- Knowledge rediscovery patterns
- Learning curve issues
- Onboarding friction points
Strategic Direction
- Project evolution trajectory
- Capability gaps
- Integration opportunities
- Future-proofing needs
Output Format
Strategic Analysis Report
# Deep Analysis: Strategic Insights
## Executive Summary
[High-level synthesis of findings]
## Key Themes Across Conversations
1. Theme with supporting evidence
2. Theme with supporting evidence
## Workflow Analysis
### Efficient Patterns
- Pattern: evidence, benefit
### Inefficiencies Identified
- Issue: frequency, impact, recommendation
## Knowledge Gaps
### Documentation Needed
- Topic: current state, recommendation
### Rules to Formalize
- Pattern: rationale, implementation
## Strategic Recommendations
1. High Impact / Low Effort
2. High Impact / Medium Effort
3. Medium-term improvements
## Action Items (Prioritized)
1. Immediate (this week)
2. Short-term (this month)
3. Strategic (this quarter)
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 · 130 lines · 53 tokens per session scan A ed38769b6cf2
conversation-deep-researcher is an agent published in the GitHub repository gpt-cmdr/ras-commander (88 stars, last pushed today), licensed MIT. It adds 53 tokens to every session and 676 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 2026-08-30.
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