一键导入
omcustom-auto-improve
Apply verified improvement suggestions from eval-core analysis to omcustom configuration
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Apply verified improvement suggestions from eval-core analysis to omcustom configuration
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | omcustom:auto-improve |
| description | Apply verified improvement suggestions from eval-core analysis to omcustom configuration |
| scope | harness |
| user-invocable | true |
| effort | high |
Reads improvement suggestions from eval-core analysis, lets the user select which to apply, applies changes in an isolated worktree with sauron verification, and creates a PR for review.
/omcustom:auto-improve # Interactive selection from pending suggestions
/omcustom:improve-report first if empty)proposed statusbun run packages/eval-core/src/cli/index.ts analyze --format json --save via BashDisplay numbered list:
[Auto-Improve] Available suggestions:
1. [HIGH] agent:lang-golang-expert — Escalate model sonnet→opus (3 failures in 5 uses)
2. [MED] routing:dev-lead-routing — Add Flutter keyword mapping (2 routing misses)
3. [LOW] skill:systematic-debugging — Add timeout guard (1 timeout in 10 uses)
Select items: [1,2,3] / "all" / "cancel"
Self-reference filter: Exclude items where targetName matches:
omcustom-auto-improve, auto-improvepipeline-guards, evaluator-optimizerFor each selected item:
proposed → approved[Approved] {N} items selected for applicationEnterWorktree tool with name auto-improve-{YYYYMMDD}Map each approved item to the appropriate subagent by targetType:
| targetType | Agent | Action |
|---|---|---|
| agent | mgr-creator | Modify agent frontmatter/body |
| skill | Matching domain expert | Revise skill SKILL.md |
| routing | general-purpose | Update routing patterns |
| model-escalation | general-purpose | Update model field in agent frontmatter |
Spawn agents in parallel (max 4 per R009). Each agent receives:
fix → re-apply with sauron feedback (max 2 cycles)reject → transition all to rejected, ExitWorktree(remove)manual → keep worktree for user inspectionchore(auto-improve): apply {N} improvement suggestionsapplied with appliedAt timestamp and PR URLExitWorktree(action: "keep") — keep branch for PR| Guard | Implementation |
|---|---|
| Self-reference prevention | Blocklist filter in Step 2 |
| User approval gate | Step 2 interactive selection |
| Worktree isolation | Step 4 EnterWorktree |
| Sauron verification | Step 6 mandatory pass |
| PR-based merge | Step 7 — no direct push to develop |
| Max items per run | 20 default, 50 hard cap |
| Max fix cycles | 2 retries before rejection |
| Rollback | git revert via mgr-gitnerd post-merge |
| Scenario | Action |
|---|---|
| No suggestions available | Display message, exit |
| User cancels selection | Exit, no state changes |
| Sauron verification fails 2x | Reject all, cleanup worktree |
| Agent application error | Mark individual item as rejected, continue others |
| EnterWorktree fails | Report error, exit |
[Auto-Improve] Starting improvement workflow
├── Suggestions: {N} available ({high}H/{medium}M/{low}L confidence)
├── Self-reference filtered: {count} items excluded
└── Select items to apply: [1,2,3] or "all" or "cancel"
[Auto-Improve] Applying {N} improvements in worktree
├── Worktree: auto-improve-{date}
├── Agents: {count} parallel
└── Pipeline guards: max 20 items, 2 retry cycles
[Auto-Improve] Verification
├── Sauron: {PASS|FAIL}
├── PR: #{number} created
└── Status: {N} items → applied
Invoke and resume YAML-defined pipelines by name — /pipeline auto-dev runs the full release pipeline
Full Self Driving — autonomous release loop that processes all auto-dev-eligible GitHub issues until none remain, by repeatedly running /pipeline auto-dev then /homework.
On explicit /homework invocation, analyze the current and linked previous sessions, extract mistakes (찐빠), and report them via omcustom-feedback with a confirmation gate. Auto-activation on session cleanup/session-end signals is OPT-IN (default OFF) — requires an explicit project/user directive. Use when explicitly auditing recent work for harness gaps.
hada.io RSS feed monitoring for AI agent/harness articles with automated /scout analysis
Pre-action boundary checking — validates agent tool calls against declared capabilities and task contracts
Auto-detect project context and optimize harness — deactivate unused agents/skills, suggest missing experts, generate project profile