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model-escalation
Advisory model escalation based on task outcome tracking
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
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Advisory model escalation based on task outcome tracking
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
6-stage structured development cycle with stage-based tool restrictions
Multi-angle release quality verification using parallel expert review teams
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.
Invoke and resume YAML-defined pipelines by name — /pipeline auto-dev runs the full release pipeline
Analyze release workflow findings and recommend follow-up actions — execute immediately or register as issues
Auto-detect project context and optimize harness — deactivate unused agents/skills, suggest missing experts, generate project profile
| name | model-escalation |
| description | Advisory model escalation based on task outcome tracking |
| scope | core |
| user-invocable | false |
Tracks task outcomes and advises reasoning-effort changes when failures are detected. Advisory-only — the orchestrator makes the final decision (R010), and OMX remains the owner of concrete model resolution.
none → minimal → low → medium → high → xhigh → ultra → max
| Condition | Action |
|---|---|
| 2+ failures with same effort for same agent type | Advise escalation |
| 3+ consecutive failures across any agent type | Advise global escalation |
| Sustained success after escalation | Advise de-escalation |
| Parameter | Default | Description |
|---|---|---|
failure_threshold | 2 | Failures before escalation advisory |
consecutive_threshold | 3 | Consecutive failures for global advisory |
cooldown_tasks | 5 | Successes before de-escalation advisory |
PostToolUse (Task) → task-outcome-recorder.sh
Records: agent_type, model_reasoning_effort, success/failure, error_summary
Storage: /tmp/.codex-task-outcomes-$PPID (JSON lines, max 50)
PreToolUse (Task) → model-escalation-advisor.sh
Reads outcomes → counts failures → advises escalation via stderr
Advisory only — never blocks, never modifies tool input
--- [Model Escalation Advisory] ---
Agent type: {agent_type}
Current effort: {current_effort}
Recent failures: {count}/{threshold}
Recommended effort: {next_effort}
Concrete model: resolved by the active OMX runtime
---
The advisor must not rewrite a concrete model ID. If the active model does not support the recommended value, the orchestrator chooses the nearest supported effort from the generated runtime model table.