stock-data-skill
OpenClaw Skill for stock data analysis
Periodic self-reflection on recent sessions. Analyzes what went well, what went wrong, and writes concise, actionable insights to the appropriate workspace files. Designed to run as a cron job.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install brennerspear/agent-self-reflectionInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
Reflect on recent sessions and extract actionable insights. Runs hourly via cron.
# List sessions active in the last 2 hours
openclaw sessions --active 120 --json
Parse the output to get session keys and IDs. Skip subagent sessions (they're task workers, not interesting for reflection). Focus on:
For each interesting session from Step 1, read the JSONL transcript:
# Read the last ~50 lines of each session file (keep it bounded!)
tail -50 ~/.openclaw/agents/main/sessions/<sessionId>.jsonl
⚠️ CRITICAL: Never load full session files. Use tail -50 or Read with offset/limit. Sessions can be 100k+ tokens.
Parse the JSONL to understand what happened. Look for:
type: "user" or type: "human" — what was askedtype: "assistant" — what you respondedtype: "tool_use" / type: "tool_result" — what tools were called and resultsFor each session, ask yourself:
Quality bar: Each insight must be:
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# Self-Reflection Skill Reflect on recent sessions and extract actionable insights. Runs hourly via cron. ## Step 1: Gather Recent Sessions ```bash # List sessions active in the last 2 hours openclaw sessions --active 120 --json ``` Parse the output to get session keys and IDs. Skip subagent sessions (they're task workers, not interesting for reflection). Focus on: - Telegram group/topic sessions (real user interactions) - Direct sessions (1:1 with Brenner) - Cron-triggered sessions (how did automated tasks go?) ## Step 2: Read Session History For each interesting session from Step 1, read the JSONL transcript: ```bash # Read the last ~50 lines of each session file (keep it bounded!) tail -50 ~/.openclaw/agents/main/sessions/<sessionId>.jsonl ``` **⚠️ CRITICAL: Never load full session files. Use `tail -50` or `Read` with offset/limit. Sessions can be 100k+ tokens.** Parse the JSONL to understand what happened. Look for: - `type: "user"` or `type: "human"` — what was asked - `type: "assistant"` — what you responded - `type: "tool_use"` / `type: "tool_result"` — what tools were called and results - Error patterns, retries, confusion ## Step 3: Analyze & Extract Insights For each session, ask yourself: ### What went well? - Tasks completed smoothly on first try - Good tool usage patterns worth reinforcing - Efficient approaches to remember ### What went wrong? - Errors, retries, wrong approaches - Misunderstandings of user intent - Tools that didn't work as expected - Context that was missing ### Lessons learned? - "Next time, do X instead of Y" - "Remember that Z works this way" - "Tool A needs parameter B or it fails" - "When user says X, they usually mean Y" **Quality bar:** Each insight must be: - **Specific** — not "be more careful" but "check if file exists before editing" - **Actionable** — something future-you can directly apply - **Non-obvious** — skip things any competent agent would know - **New** — don't repeat insights already captured ## S...
OpenClaw Skill for stock data analysis
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