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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
| name | opencode-conversation-analysis |
| description | 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. |
| compatibility | OpenCode-specific. Supports SQLite-backed storage (`opencode.db`) with fallback to legacy JSON storage. Requires bash and python3. |
Analyze user messages from OpenCode sessions to identify recurring themes, communication patterns, and steering behaviours.
~/.agents/skills/opencode-conversation-analysis/scripts/extract.sh
This script:
~/.local/share/opencode/opencode.db (SQLite) when available~/.local/share/opencode/storage/ for older OpenCode installs/tmp/opencode-analysis/chunk_*.jsonlReview the output summary to see how many chunks were created.
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).
Once all subagents return:
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.
The user may request:
CHUNK_SIZE in extract.sh (default 320000 chars)MIN_TEXT_LEN in extract.sh (default 10 chars)parent_id IS NULL (SQLite) and parentID (legacy) filters in extract.shSee references/storage-format.md for details on OpenCode's conversation storage structure.
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