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Auto Improving Agent

作者 omaression · GitHub ↗ · v1.0.0-alpha · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install auto-improving-agent
功能描述
Automatically capture corrections, failures, and reusable discoveries into `.learnings/` files using signal-based filtering. Triggers when the user corrects...
使用说明 (SKILL.md)

Self-Improving Agent

Capture what matters. Ignore noise. Promote proven patterns. Automate all of it.

Source of truth

  • .learnings/LEARNINGS.md — corrections, env configs, reusable fixes, architecture decisions
  • .learnings/ERRORS.md — tool/command failures with fixes
  • .learnings/FEATURE_REQUESTS.md — missing capabilities worth tracking
  • .learnings/ARCHIVE.md — entries scored out during retention sweeps (never injected into context, but searchable)

Write gate

Before logging anything, the candidate must pass at least ONE filter:

Filter Weight Description
Correction ALWAYS Omar explicitly corrected the agent
Recurrence HIGH Same issue hit 2+ times (check existing entries)
Cost-to-rediscover HIGH Would take >2 tool calls to figure out again
Blast radius MEDIUM Affects multiple skills, projects, or workflows
Decay risk MEDIUM Non-obvious env/config detail that changes rarely

If NONE match → do not log. This replaces any arbitrary line-count threshold.

Never log:

  • routine successes
  • facts obvious from docs or code
  • one-off tasks with no recurrence potential
  • anything already in MEMORY.md, SOUL.md, USER.md, or AGENTS.md

Entry format

LEARNINGS.md:

- [YYYY-MM-DD] [Category]: [Actionable takeaway]

Categories: Correction, Env, Workflow, Testing, Skills, Git, Architecture

ERRORS.md:

- [YYYY-MM-DD] [Tool]: [What failed] → [Fix]

Mark fixed items with [fixed]. Delete stale entries during retention sweeps.

Retention gate

Instead of a hard line cap, score each entry periodically:

Signal Score
Referenced or applied in last 30 days +3
Matches active project context +2
Direct correction from Omar +2
Has prevented a repeat error +3
Env/config still valid +1
Superseded by newer entry −5
>90 days old, never referenced −3

Action:

  • score ≥ 2 → keep
  • 0 ≤ score \x3C 2 → archive to .learnings/ARCHIVE.md
  • score \x3C 0 → delete

Run this sweep during heartbeat maintenance (every ~3 days) or when LEARNINGS.md feels noisy.

Automated triggers

These fire without user prompting:

  1. Post-task scan: After multi-step tasks, check for retried commands, error→workaround sequences, or avoidable file reads. If found, evaluate against write gate and log if it passes.

  2. Session-start sweep: On .learnings/LEARNINGS.md read, flag entries >90 days old for retention scoring.

  3. Promotion detector: After logging, scan for entries with the same [Category] tag appearing 3+ times. If found, auto-suggest a one-liner promotion to:

    • behavior/style → SOUL.md
    • workflow/process → AGENTS.md
    • tool/env gotcha → TOOLS.md

  4. Cross-session pattern detection: When memory_search returns a daily note describing a workaround, check if .learnings/ already has it. If not and it passes the write gate, log it.

Dedup

Before logging, scan existing entries for near-duplicates. If the lesson already exists, only update it if the new version is sharper or more general.

Quality bar

Every entry must help a future session avoid wasted work in under one glance.

安全使用建议
This package appears to be what it says: a local helper that suggests and scores learnings and adds a bootstrap reminder. A few practical notes before enabling: - The SKILL.md promises automated background triggers (post-task scans, periodic sweeps). The code included only provides a bootstrap reminder and a local retention_scorer.py utility; automatic capture would rely on the agent following the instructions or additional hook implementations. If you expect fully automatic logging, verify how your agent/platform will implement those triggers. - The skill will read and write files under .learnings/ and may modify LEARNINGS.md when the scorer is run (retention_scorer.py). Run the scorer with --dry-run first to see planned changes before letting it write files. - There are no network calls or credential requests in the provided files, but the instructions do tell the agent to consult workspace files and memory_search. If your workspace contains secrets or sensitive files, decide whether you want the agent to index or copy that content into .learnings/ (the skill's write gate discourages logging obvious facts or single-use items, but that's an instruction — not an enforced safeguard). - Review the retention_scorer.py and the hook files yourself (they are small) and run the included tests locally if you want assurance about behavior. If you accept that the agent will be permitted to read workspace files and write to .learnings/, this skill is coherent and low-risk. If you require true autonomous capturing of failures without additional platform support, request or implement the missing event handlers instead of relying on the SKILL.md promises.
功能分析
Type: OpenClaw Skill Name: auto-improving-agent Version: 1.0.0-alpha The skill bundle provides a structured framework for an OpenClaw agent to manage its own 'learnings' and error logs within a `.learnings/` directory. It includes a Python maintenance script (`scripts/retention_scorer.py`) for log rotation and TypeScript/JavaScript hooks to inject behavioral reminders during the agent's bootstrap process. The logic is transparent, localized to the workspace, and lacks any indicators of data exfiltration, malicious execution, or harmful prompt injection.
能力评估
ℹ Purpose & Capability
The README/SKILL.md describes automated, signal-driven logging (post-task scans, session sweeps, promotion detector). The provided hook code only injects a virtual SELF_IMPROVEMENT_REMINDER.md at agent bootstrap and the included Python script is a local retention scorer. There is no code here that actually implements background 'post-task' automatic capture or an autonomous logger; that behavior would have to be implemented by the agent following the SKILL.md instructions or by additional platform hooks. This is a mild incoherence between the claimed autonomous triggers and the code present, but not malicious.
✓ Instruction Scope
SKILL.md instructs scanning and writing files under .learnings/, performing retention scoring, and using memory_search to detect cross-session patterns. All of these actions are within the stated purpose (capturing learnings) and the code supports the retention scoring and bootstrap reminder. The instructions do ask the agent to read workspace files and memory_search results (expected for this feature) but do not direct data to external endpoints or request unrelated system credentials.
✓ Install Mechanism
No install spec and no external downloads — the skill is instruction/code-only. The included hook and Python script are local files (no network fetch or archive extraction). This is low-risk from an installation perspective.
✓ Credentials
The skill requests no environment variables, no credentials, and no config paths. Its operations are limited to reading/writing .learnings/ files and using platform memory_search; that scope is proportional to the stated purpose.
✓ Persistence & Privilege
always:false (default), and the hook only injects a virtual reminder at agent bootstrap. The skill does not request permanent platform-wide privileges, does not modify other skills' configs, and does not enable itself automatically across all agents.
如何使用
  1. 确保已安装 OpenClaw（本地或 Docker 部署）
  2. 在对话框中输入安装命令：/install auto-improving-agent
  3. 安装完成后，直接呼叫该 Skill 的名称或使用 /auto-improving-agent 触发
  4. 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0-alpha
Initial release of self-improving-agent — automates capturing actionable corrections, errors, and discoveries to `.learnings/` files. - Logs only high-value learnings, errors, and requests using a targeted signal-based filter. - Periodically scores and prunes logged data to retain only proven, still-relevant entries. - Promotes recurring or transferable lessons to higher-level skill, agent, or tool docs. - Deduplicates and sharpens entries for high-impact, future-proof guidance. - Automates all triggers to minimize user intervention and ensure continuous improvement.
元数据
Slug auto-improving-agent
版本 1.0.0-alpha
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Auto Improving Agent 是什么？

Automatically capture corrections, failures, and reusable discoveries into `.learnings/` files using signal-based filtering. Triggers when the user corrects... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 167 次。

如何安装 Auto Improving Agent？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install auto-improving-agent」即可一键安装，无需额外配置。

Auto Improving Agent 是免费的吗？

是的，Auto Improving Agent 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

Auto Improving Agent 支持哪些平台？

Auto Improving Agent 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 Auto Improving Agent？

由 omaression（@omaression）开发并维护，当前版本 v1.0.0-alpha。

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