omnimind-ai/omnibot/self-improving-agent

Self Improving Agent

This built-in skill is fixed-injected for Omnibot agent runs.

Use it to maintain a lightweight learning loop without interrupting the user's main task.

The runtime may auto-read this skill after a tool failure and auto-record the failure into data/ERRORS.md. Repeated failures with the same signature are merged into one bounded entry. If an argument/schema failure is followed by a successful call to the same tool in the same Agent run, the runtime closes that pending entry and distills the verified recovery into short-term memory.

When To Record

Record after the immediate task is safe or complete when any of these happens:

1. a non-trivial command, tool, browser, or device action fails
2. the user corrects your understanding, path, rule, or project assumption
3. you discover an outdated Omnibot/runtime/project convention
4. you find a reusable workaround or best practice that will likely save future retries
5. the same mistake repeats in the same task or across tasks

Do not record ordinary chat, tiny one-off slips, or anything the user asked not to save.

Default Storage

• skill-local learnings: .omnibot/skills/self-improving-agent/data/
• project-local learnings: <project>/.learnings/ only when the lesson is repo-specific
• long-term memory: .omnibot/memory/MEMORY.md via memory_upsert_longterm
• short-term memory: .omnibot/memory/short-memories/ via memory_write_daily

Logging Workflow

1. Finish or stabilize the current user-facing step first.
2. Prefer the bundled scripts/omnibot_auto_log.sh for structured logging because it keeps IDs, headers, and append rules consistent.
3. Use skill scope by default.
4. Switch to --project /workspace/<repo> only when the lesson is clearly tied to one repository.
5. Use learning for corrected knowledge or best practices.
6. Use error for concrete failures with stderr, HTTP errors, stack traces, or invalid assumptions.
7. Use feature for recurring capability gaps the user actually wants.
8. Use promote <ENTRY_ID> only after the lesson looks reusable across tasks.

Memory Promotion

As soon as you actually fix a failure — or the same failure recurs — write one short "遇到 X 先 Y" rule to memory (memory_write_daily, or memory_upsert_longterm when it is broadly stable) and back-fill the ERRORS entry's 建议修复 and 状态 (pending → resolved). Do not leave a resolved failure sitting as pending with an empty fix.

The runtime can automatically close a same-run argument/schema failure after verified success. An execution/runtime failure remains pending and still needs a concrete fix from you before promotion. Do not promote a generic “retry succeeded” observation as a stable rule.

Promote a lesson into memory only when it is stable, short, and broadly reusable.

Good candidates:

• a rule like “遇到 X 先检查 Y”
• a stable workspace convention
• a long-term user preference the user explicitly wants remembered

Prefer this order:

1. log into the skill data first
2. promote to the skill public area if it becomes broadly reusable
3. write the distilled rule with memory_write_daily or memory_upsert_longterm

Do not invent Minis-only paths or tools such as /var/minis/... or memory_write.

Recall

Recorded failures and lessons are indexed into memory retrieval. Before retrying a tool, command, or environment step that has failed before — or when the injected memory context mentions a related pitfall — trust that recall (or call memory_search) and apply the known fix instead of repeating the failed step.

Command Patterns

Use the bundled script through sh:

sh <scriptsDir>/omnibot_auto_log.sh init
sh <scriptsDir>/omnibot_auto_log.sh learning "摘要" "详情"
sh <scriptsDir>/omnibot_auto_log.sh error "摘要" "错误输出"
sh <scriptsDir>/omnibot_auto_log.sh feature "能力缺口" "用户背景"
sh <scriptsDir>/omnibot_auto_log.sh --project /workspace/my-repo learning "摘要" "详情"
sh <scriptsDir>/omnibot_auto_log.sh search 关键词
sh <scriptsDir>/omnibot_auto_log.sh promote LRN-20260409-ABC

If you need to refine an existing entry instead of appending a new one, use read and edit.

Output Discipline

• keep summaries short and specific
• include the concrete command/tool/context that failed
• include the corrected rule, not only the symptom
• avoid logging secrets, tokens, and personal data