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self-reflection

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.

brennerspear0 source installs☆ 0 Repository starsv1.0.0

Installation

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-reflection

Inspect 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.

View source ↗Guides

Skill instructions

Self-Reflection Skill

Reflect on recent sessions and extract actionable insights. Runs hourly via cron.

Step 1: Gather Recent Sessions

# 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:

# 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...

Source information

Source
github
Repository
openclaw/skills
Path
skills/brennerspear/agent-self-reflection
Version
1.0.0
Commit
a4c89656797cd75d3edbcc3e84b6856429be018d
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# 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...

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