BrennerSpear

Self Reflection

Life Servicev1.0.0
By BrennerSpearUpdated 2026-05-17agent-self-reflection@clawhub_brennerspear/agent-self-reflection
Personal PlanningHabits & Productivity#Cron Job

According to AlphaMoat, as of 2026-09, Self Reflection has 3,405 downloads and 0 stars — ranked #3,289 of 5,078 AI skills overall, and #159 of 246 in Life Service.

Installs
101
Downloads
3.4K
Stars
-
Overall Rank
#3,289
Top 65% overall · Top 65% in Life Service
PlatformInstallsDownloadsStarsComments
ClawHub ↗1013.1K--
SkillHub ↗973.4K-—

Platforms count differently and update at different times.

What this skill does

The workflow reads portions of session transcripts and may persist findings in high-trust files. Transcripts can be private, and derived guidance may be inaccurate or sensitive. Limit the eligible sessions, inspect only bounded excerpts, redact sensitive content, and require owner review before editing AGENTS.md, TOOLS.md, MEMORY.md, or skill files.

Profile published 2026-10-29

What you can do with it

  • Plan a bounded review of recent sessions.
  • Extract specific, actionable, non-duplicative lessons.
  • Design review and redaction steps before writing to workspace files.

When to use it

  • When reviewing recent agent sessions for operational lessons.
  • When routing useful findings to instruction, tool, memory, or skill files.
  • Before enabling scheduled runs or editing high-trust files, when transcript scope, redaction, and owner review need to be set.

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

Summarize how Self-Reflection reads recent sessions and extracts lessons, including safeguards before writing files.
Design a session-review workflow that produces drafts without modifying workspace files.

Monthly Trend

Downloads3.4K
1.3K2.4K3.6K2026-03 · Downloads 1.5K2026-05 · Downloads 2.3K2026-07 · Downloads 2.8K2026-08 · Downloads 3.0K2026-09 · Downloads 3.4K26-0326-09
Stars-
0112026-03 · Stars -2026-05 · Stars -2026-07 · Stars -2026-08 · Stars -2026-09 · Stars -26-0326-09

The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change. Other skills share this name; figures from before the change are attributed to this one, which currently has the most downloads.

MonthDownloadsMoMStars
2026-093.4K—-
2026-083.0K+5.1%-
2026-072.8K—-
2026-052.3K—-
2026-031.5K—-

SKILL.md (original)

The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.

4.7 KB · ClawHub v1.0.0
---
name: self-reflection
slug: agent-self-reflection
version: 1.0.0
description: 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.
---

# Self-Reflection Skill

Reflect on recent sessions and extract actionable insights. Runs hourly via cron.
Show all (142 lines)

## 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

## Step 4: Route Insights to the Right Files

Each insight belongs somewhere specific. Route them:

### → `AGENTS.md`
- Process improvements (how to handle sessions, memory, etc.)
- New conventions or workflow rules
- Safety lessons

### → `TOOLS.md`
- Tool-specific gotchas ("gog needs --json flag for parsing")
- Environment details (paths, configs, quirks)
- New tool patterns discovered

### → `memory/YYYY-MM-DD.md` (today's date)
- Session-specific context ("Brenner asked about X project")
- Temporary facts that matter today but not forever
- What happened today (events, decisions, requests)

### → `memory/about-user.md`
- New preferences discovered
- Communication style observations
- Project/interest updates

### → `skills/<skill-name>/SKILL.md`
- Improvements to specific skill instructions
- Bug fixes in skill workflows
- New parameters or approaches for a skill

### → `MEMORY.md`
- Updates to the memory index if new memory files are created

## Step 5: Write the Insights

For each insight, append or edit the appropriate file. Use the `Edit` tool for surgical changes to existing content. Use append (write to end) for daily memory files.

**Format for daily memory files:**
```markdown
## Self-Reflection — HH:MM ET

### Insights
- [source: session-key] Lesson learned here
- [source: session-key] Another insight

### Tool Notes
- Discovered: tool X needs Y configuration

### User Context
- Brenner mentioned interest in Z
```

## Step 6: Summary

After writing all insights, produce a brief summary of what you reflected on and what you wrote. This is your output — keep it to 2-4 sentences max.

If there's nothing interesting to reflect on (quiet period, only heartbeats), just say so. Don't manufacture insights.

## Quality Checklist

Before writing any insight:
- [ ] Is this actually new? (Check existing files first)
- [ ] Is this specific and actionable?
- [ ] Am I routing it to the right file?
- [ ] Am I keeping daily memory files concise (not dumping full transcripts)?
- [ ] Did I respect the token budget (no huge file reads)?

## Anti-Patterns (Don't Do These)

- ❌ Don't summarize every session — only extract *lessons*
- ❌ Don't read full JSONL files — tail/limit only
- ❌ Don't write vague insights ("improve response quality")
- ❌ Don't duplicate existing knowledge
- ❌ Don't create new files when appending to existing ones works
- ❌ Don't reflect on your own reflection sessions (skip cron:self-reflection sessions)

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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.