Install
openclaw skills install @jiesou/self-reflectionUse when you need to determine the accuracy of the answer.
openclaw skills install @jiesou/self-reflectionYou probably got most things right. This skill catches the few things you got wrong.
flag claims → verify in parallel → fix → loop (optional) → done
Scan your previous output (or user-specified statements) for every factual claim. A factual claim must be small and verifiable — a number, a causal statement, an assertion, a relationship. Skip vague or subjective statements.
For each flagged claim, launch a subagent.
Every subagent's prompt template as below:
You are an independent auditor. Verify the accuracy of the small claim below.
Claim: [exact claim]
Context: [relevant context]
- Answer ONLY from your own tool calls. Do NOT rely on prior knowledge.
**Don't just web search — fetch full sources.** Search's preview are cherry-picked and incomplete.
- Match tool to source: read PDFs, use gh CLI for full issue/PR threads, use yt-dlp Skill for every video content
Output a brief audit result:
- Claim Accuracy: yes / partial / no
- Confidence: 0–10
If not accurate:
- Evidence: quote or summarize what was found
- Reasoning: 1–2 sentences
After all claims are verified, correct every wrong claim:
If the user needs a fully correct output (not just a /self-reflection), run the Loop workflow to iteratively verify and fix until the output is indisputably correct.
Start todowrite to track each iteration "Round":
R1: flag 5 claims
R1: verify batch 1 (3 subagents, claims 1–3)
R1: verify batch 2 (2 subagents, claims 4–5)
R1: fix statements
R2: flag 3 claims
R2: verify batch (3 subagents, claims 1–3)
R2: fix statements
R2: done — output is correct
Update items in your todo list anytime. Stop when the output is indisputably correct.