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self-reflection

该 Skill 用于自查输出准确性:先标记出可验证的小颗粒事实性主张,再为每条主张启动独立上下文的子代理并行核验,依据工具调用结果判定准确性并给出置信度,随后修正错误、补充限定条件或标注不可靠之处,并可选循环迭代直至输出无误。核验强调读取原始完整来源而非检索预览。

jiesou
jiesou
热度 48.33
最近更新 2026-08-16 22:52:08
分类:效率依赖:内部调用大模型 / 联网浏览搜索 / 代码执行应用场景:研究 / 校对润色 / 工作流

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add jiesou/self-reflection
Metadata
name
self-reflection
description
Use when you need to determine the accuracy of the answer.

Self-Reflection

You probably got most things right. This skill catches the few things you got wrong.

Quick Flow

flag claims → verify in parallel → fix → loop (optional) → done

How-To

1. Flag

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.

2. Verify

For each flagged claim, launch a subagent.

  • Each subagent runs in an isolated, independent context — no access to chat history or prior tool results, context should be fair and unbiased.
  • Verify in parallel: Fire all subagents simultaneously in a single message !

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

3. Fix

After all claims are verified, correct every wrong claim:

  • Fix obvious errors directly.
  • Back corrected statements with clear evidence.
  • Strengthen weak statements by adding qualifiers or conditions.
  • Clearly flag any unsupportable claims as unreliable.

4. Loop (Optional)

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.