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Active Self-Improvement

该 Skill 在会话结束、批处理或定时触发时读取错误记录、学习日志、记忆和项目文件，按 SCAN、PROPOSE、APPLY 三阶段识别重复错误、重复纠正、知识过时等模式，生成带证据与风险等级的变更提案。低风险变更直接应用，高风险变更等待批准，并支持 dry-run 仅输出报告。

dodge1218
dodge1218
热度 59.15
最近更新 2026-05-18 12:38:35
分类：知识依赖：读写文件 / 内部调用大模型应用场景：工作流 / 笔记整理 / 任务管理 / 报告撰写

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 active-self-improvement
Metadata
name
active-self-improvement
version
1.2.0
description
Active self-improvement loop that reads learnings, errors, batch outputs, and memory — detects patterns — and UPDATES skills/protocols/behavior automatically. Use when the agent should get smarter without being prompted. Different from passive logging — this ACTS on what it learns. Triggers after Recorder at end of sessions, after batch processing, after project milestones, on explicit "improve" or "what have we learned" prompts, or on a weekly cron schedule.

Auto-Improve

Reads logs, detects patterns, rewrites the playbook. Not passive logging — this ACTS on what it learns.

SCAN (read logs) ──► PROPOSE (specific edits) ──► APPLY (low-risk auto, high-risk flag)

Input Sources

Source What It Contains
.learnings/ERRORS.md What broke and how it was fixed
.learnings/LEARNINGS.md Corrections, insights, knowledge gaps, batch outcomes
workspace/OUTSTANDING.md Ranked ideas and opportunities
memory/permanent/*.md Current knowledge state
workspace/DELEGATION_PLAN.md Atom timing data (if delegation was used)

Step 1: SCAN

Detect:

  • Repeated errors — same mistake 3+ times → needs a prevention rule
  • Repeated corrections — user keeps fixing the same thing → behavior change needed
  • Emerging patterns — 3+ items connecting → thesis forming
  • Stale knowledge — facts in permanent memory contradicted by recent sessions
  • Unused wins — high-value items that haven't been acted on

Step 2: PROPOSE

For each detected pattern:

PROPOSAL: [short title]
EVIDENCE: [file#line references]
CHANGE: [exact edit — old text → new text]
RISK: [low/medium/high]
REVERSIBLE: [yes/no]
Pattern-Key: [hash(error+fix) for dedup]
Pattern Type Action Target File
Repeated error Add prevention rule relevant skill's ## Learned section
Repeated correction Update behavior guideline SOUL.md or AGENTS.md
Emerging thesis Write thesis + next steps OUTSTANDING.md
Stale knowledge Update the fact memory/permanent/*.md
Unused win Create ticket or reminder NEXT_TICKET.md or cron

Step 3: APPLY

  • Low risk + reversible: Apply immediately. Log the change.
  • Medium risk: Apply but notify user on next interaction.
  • High risk: Write to OUTSTANDING.md and wait for approval.
  • Dry-run mode (--dry-run): Propose all changes but apply none. Output a report.

Use 3-occurrence threshold before proposing pattern-based changes. Track recurrence with Pattern-Key and Recurrence-Count.

Error→Skill Feedback Loop

After SCAN, for each error in ERRORS.md:

  1. Extract the Context column value
  2. Match against skill names (fuzzy: "SiteBlitz CSS" → webdev-sop)
  3. If match found and skill doesn't already have the fix in ## Learned: 
    ## Learned
- [date] [error summary] → [fix]. Source: .learnings/ERRORS.md#L[N]
  4. Use Pattern-Key: hash(error+fix) to prevent duplicates

Skills self-heal: every failure improves the relevant skill.

Delegation Feedback

After delegation plan completes:

  1. Read atom timing data from DELEGATION_PLAN.md
  2. Atom actual time > 2× estimated → flag estimation drift
  3. Atom model upgraded (flash→sonnet) → update routing suggestion in MODEL_ROUTING_PROTOCOL.md
  4. Append summary to .learnings/LEARNINGS.md