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OpenClaw

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 installations selon la source☆ 0 Étoiles du dépôtv1.0.0

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install brennerspear/agent-self-reflection

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

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...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/brennerspear/agent-self-reflection
Version
1.0.0
Commit
a4c89656797cd75d3edbcc3e84b6856429be018d
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

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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