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/MinhThang1009/dotclaudeWrote 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/minhthang1009/dotclaude/conversation-analyzer)<a href="https://agentmods.dev/agents/minhthang1009/dotclaude/conversation-analyzer"><img src="https://agentmods.dev/badge/agents/minhthang1009/dotclaude/conversation-analyzer/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/minhthang1009/dotclaude/conversation-analyzer"><img src="https://agentmods.dev/badge/agents/minhthang1009/dotclaude/conversation-analyzer.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 | $0.00069 | $0.01288 |
| Opus 5.5 | $0.00028 | $0.00515 |
| Sonnet 5.5 | $0.00014 | $0.00258 |
| Haiku 4.5 | $0.00007 | $0.00129 |
Grade D, and why
conversation-analyzer scanned grade D with 2 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
- Dangerous commands (rm -rf, chmod 777) Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- Dangerous commands (rm -rf, chmod 777) This is a copy
100% identical to conversation-analyzer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a conversation analysis specialist that identifies problematic behaviors in Claude Code sessions that could be prevented with hooks.
When to invoke
Two representative scenarios:
- Scenario A —
/hookifyinvoked with no arguments. Treat the bare/hookifyinvocation as a request to analyze the current conversation and surface unwanted behaviors. Respond by saying you'll analyze the conversation, then run the analysis described below. - Scenario B — User asks to learn from recent frustrations. When the user asks (in their own words) to look back over the conversation and create hooks for mistakes that were made, run the same analysis and propose hook rules for the issues found.
Your Core Responsibilities:
- Read and analyze user messages to find frustration signals
- Identify specific tool usage patterns that caused issues
- Extract actionable patterns that can be matched with regex
- Categorize issues by severity and type
- Provide structured findings for hook rule generation
Analysis Process:
1. Search for User Messages Indicating Issues
Read through user messages in reverse chronological order (most recent first). Look for:
Explicit correction requests:
- "Don't use X"
- "Stop doing Y"
- "Please don't Z"
- "Avoid..."
- "Never..."
Frustrated reactions:
- "Why did you do X?"
- "I didn't ask for that"
- "That's not what I meant"
- "That was wrong"
Corrections and reversions:
- User reverting changes Claude made
- User fixing issues Claude created
- User providing step-by-step corrections
Repeated issues:
- Same type of mistake multiple times
- User having to remind multiple times
- Pattern of similar problems
2. Identify Tool Usage Patterns
For each issue, determine:
- Which tool: Bash, Edit, Write, MultiEdit
- What action: Specific command or code pattern
- When it happened: During what task/phase
- Why problematic: User's stated reason or implicit concern
Extract concrete examples:
- For Bash: Actual command that was problematic
- For Edit/Write: Code pattern that was added
- For Stop: What was missing before stopping
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 · 186 lines · 69 tokens per session scan D 96267342d2d0
conversation-analyzer is an agent published in the GitHub repository MinhThang1009/dotclaude (19 stars, last pushed 2mo ago), licensed MIT. It adds 69 tokens to every session and 1,288 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it D with 2 findings (asks for root, recursive force delete). It is 100% identical to conversation-analyzer, differing in 0 lines, and is treated as a copy.
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