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-insights-orchestrator.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-insights-orchestrator)<a href="https://agentmods.dev/agents/gpt-cmdr/ras-commander/conversation-insights-orchestrator"><img src="https://agentmods.dev/badge/agents/gpt-cmdr/ras-commander/conversation-insights-orchestrator/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-insights-orchestrator"><img src="https://agentmods.dev/badge/agents/gpt-cmdr/ras-commander/conversation-insights-orchestrator.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 | $0.00064 | $0.01155 |
| Opus 5.5 | $0.00026 | $0.00462 |
| Sonnet 5.5 | $0.00013 | $0.00231 |
| Haiku 4.5 | $0.00006 | $0.00115 |
Grade A, and why
conversation-insights-orchestrator 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conversation Insights Orchestrator
Coordinate comprehensive analysis of Claude Code conversation history by dispatching to specialized sub-agents.
Primary Sources
Conversation Data:
~/.claude/history.jsonl-- Prompt index (lightweight, all projects)~/.claude/projects/{encoded-path}/*.jsonl-- Full conversation files
Python Utilities:
scripts/conversation_insights/conversation_parser.py-- Parsing utilitiesscripts/conversation_insights/pattern_analyzer.py-- Pattern detectionscripts/conversation_insights/insight_extractor.py-- Insight extractionscripts/conversation_insights/report_generator.py-- Report generation
Architecture
Orchestrator (Sonnet)
├── Index Scanner (Haiku) - Fast scanning of history.jsonl
├── Pattern Analyzer (Haiku) - N-gram and pattern detection
├── Blocker Detector (Sonnet) - Problem/solution extraction
├── Best Practice Extractor (Sonnet) - Practice identification
├── Deep Researcher (Opus) - Complex synthesis (when needed)
└── Report Generator (Sonnet) - Final report compilation
Orchestration Workflow
Phase 1: Index Scan
- Read
~/.claude/history.jsonl - Parse JSON lines to extract prompts with timestamps
- Filter by lookback period (default: 7 days)
- Identify projects and conversation counts
Phase 2: Pattern Analysis
- Extract all user prompts from period
- Run frequency analysis on n-grams
- Match against known slash command patterns
- Identify project activity distribution
Phase 3: Insight Extraction (for detailed reports)
- Select high-value conversations (long, complex)
- Extract problem-solution pairs
- Identify design patterns and anti-patterns
- Extract best practices
Phase 4: Report Generation
- Compile findings from all phases
- Generate markdown report
- Save to
agent_tasks/directory - Return summary to user
Lookback Period Strategy
| Period | Analysis Depth | Focus |
|---|---|---|
| 24 hours | Full detail | All messages, tool calls |
| 7 days | Detailed | User prompts, key responses |
| 30 days | Summarized | Conversation summaries |
| 90 days | High-level | Pattern detection only |
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 · 147 lines · 64 tokens per session scan A fd086d3ddc5e
conversation-insights-orchestrator is an agent published in the GitHub repository gpt-cmdr/ras-commander (88 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 1,155 once invoked, about $0.0003 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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