opencode-conversation-analysis
Analyze OpenCode conversation history to identify themes and patterns in user messages. Use when asked to analyze conversations, find themes, review how a user steers agents, or extract insights from session history.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/connorads/dotfiles --skill opencode-conversation-analysis- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads opencode-conversation-analysis automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Conversation Analysis
Analyze user messages from OpenCode sessions to identify recurring themes, communication patterns, and steering behaviours.
Critical Rules
- NEVER cat or read chunk files directly - they're huge and will explode your context
- Pass file paths to subagents - let them read and analyze independently
- Use parallel subagents - one per chunk, they run concurrently
- Subagents return structured JSON - you synthesize at the end
Workflow
Step 1: Run Extraction
~/.agents/skills/opencode-conversation-analysis/scripts/extract.sh
This script:
- Reads from
~/.local/share/opencode/opencode.db(SQLite) when available - Falls back to
~/.local/share/opencode/storage/for older OpenCode installs - Extracts user messages from main sessions with metadata (session_id, title, timestamp, text)
- Filters out messages < 10 characters
- Chunks into ~320k char files (~80k tokens each)
- Outputs to
/tmp/opencode-analysis/chunk_*.jsonl
Review the output summary to see how many chunks were created.
Step 2: Launch Parallel Subagents
For each chunk file, spawn a general subagent with this prompt template:
Read the file /tmp/opencode-analysis/chunk_N.jsonl which contains user messages from coding sessions (JSONL format with fields: session_id, session_title, timestamp, text).
Analyze these messages to identify recurring themes in how the user steers/guides AI coding assistants. Look for patterns like:
- How they give feedback
- How they correct mistakes
- How they scope/refine requests
- Communication style preferences
- Technical approaches they emphasize
For each theme you identify, provide:
1. Theme name (short, descriptive)
2. Description (1-2 sentences)
3. 2-3 direct quote examples from the messages
Return ONLY valid JSON in this format:
{
"themes": [
{
"name": "Theme Name",
"description": "Description of the pattern",
"examples": ["quote 1", "quote 2"]
}
]
}
Launch ALL chunk subagents in parallel (single message, multiple Task tool calls).
Step 3: Synthesize Results
Once all subagents return:
- Collect all theme objects from all chunks
- Group similar themes (same name or overlapping descriptions)
- Merge examples from duplicate themes
- Rank themes by how many chunks they appeared in
- Pick the best 2-3 examples per theme
Step 4: Output Format
Present the final analysis as markdown with this structure:
# Themes in How You Steer AI Coding Assistants
Analysis of N messages across M sessions (date range)
---
## 1. Theme Name
Description of the pattern.
**Examples:**
- "direct quote 1"
- "direct quote 2"
- "direct quote 3"
---
## 2. Next Theme
...
Output directly to the user - don't write to a file unless asked.
Customisation Options
The user may request:
- Different chunk sizes: Edit
CHUNK_SIZEin extract.sh (default 320000 chars) - Different message filter: Edit
MIN_TEXT_LENinextract.sh(default 10 chars) - Include subagent sessions: Remove the
parent_id IS NULL(SQLite) andparentID(legacy) filters in extract.sh - Time period filtering: Add timestamp filtering in extract.sh
Storage Format Reference
See references/storage-format.md for details on OpenCode's conversation storage structure.
FAQ
What does the opencode-conversation-analysis skill do?
Analyze OpenCode conversation history to identify themes and patterns in user messages. Use when asked to analyze conversations, find themes, review how a user steers agents, or extract insights from session history.
How do I install opencode-conversation-analysis?
Run npx skills add https://github.com/connorads/dotfiles --skill opencode-conversation-analysis in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/opencode-conversation-analysis/ (personal) or .claude/skills/opencode-conversation-analysis/ (project).
Which AI agents can use opencode-conversation-analysis?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is opencode-conversation-analysis safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains opencode-conversation-analysis?
It is published by connorads in connorads/dotfiles. It has 92 recorded installs.
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