# Gpt Model Effort Advisor > Recommend which GPT-5.6 model tier (Luna/Terra/Sol) and reasoning effort (Low..Ultra) fits a task, learn from the user's own successes and failures, diagnose quality problems, and report the live session's model and effort. Advisory — it proposes a grounded starting point and an escalation rule; the human decides and switches. Load when the user asks which model or effort to use, starts a substantial task without deliberately choosing a tier, reports a poor or unsatisfying result, wants to reduce cost without losing quality, or asks what model the session is running. Triggers — "which model", "which effort", "reasoning effort", "cut cost", "is a bigger model worth it", "do I need max/ultra", "result is bad", "quality dropped", "what model am I on", "record this combo". - Skill: `dandacompany/gpt-model-effort-advisor` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add dandacompany/gpt-model-effort-advisor` - Raw SKILL.md: https://api.skillmd.com/api/skills/dandacompany/gpt-model-effort-advisor/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: dandacompany (https://skillmd.com/u/dandacompany) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dandacompany/gpt-model-effort-advisor --- # GPT-5.6 Model & Effort Advisor Help pick the right **model tier** and **reasoning effort** for a task, escalate only when needed, learn from real outcomes, and diagnose failures. It recommends and records; the user decides and switches. ## Two independent axes - **Model tier = intelligence ceiling.** A larger tier knows more and reasons deeper, at a higher price per token. GPT-5.6 tiers: **Luna** (economy) → **Terra** (balanced) → **Sol** (frontier). - **Reasoning effort = deliberation time.** More effort spends more thinking on the same problem, at more tokens. Levels: **Low → Medium → High → Max → Ultra**. These are different dials. Turning both to the maximum by default wastes money without improving quality on most tasks. Choose each axis deliberately. ## Engine: `registry.py` `registry.py` (same folder, standard library only) powers the three data functions. Always use this CLI — do not hand-edit the JSON. ``` python3 registry.py session # read the live session's model + effort python3 registry.py query "" [--axes ...] # find similar past successes/failures python3 registry.py record --task "..." --model gpt-5.6- --effort \ --outcome success|fail|partial [--axes ...] [--reason ...] [--cost N] [--minutes N] [--notes ...] python3 registry.py stats # summary of the registry ``` The registry starts empty and fills from the user's real usage. **Recorded outcomes take priority over the static heuristics below** — personal history beats generic rules over time. ## Workflow for every task ### 1. Check the session (function: state) ``` python3 registry.py session → {"model": "...", "effort": "...", "source": "..."} ``` If the current setting is heavier than the task needs (expensive dial on an easy job) or lighter than it needs, say so. Codex does not auto-switch per turn — the user switches with `/model` or launches the next session with `-m`/`-c`. ### 2. Recommend (registry first, heuristics second) ``` python3 registry.py query "" --axes "<4-axis tags>" ``` - If `best_success_combos` exist, recommend that combo first ("a similar task succeeded with Terra + High"). - If `avoid_failures` exist, warn against those combos. - If the registry has no similar record, fall back to the heuristics below. Classify the task on **four axes** and pass them so matching is precise: `--axes "verifiable=yes|partial|no,failcost=low|mid|high,volume=high|low,depth=shallow|mid|deep"` - **verifiable** — can success be checked mechanically (tests, schema, exact numbers, render errors)? - **failcost** — how costly is a wrong answer? - **volume** — how many times will this run (batch → price-sensitive)? - **depth** — deep world-knowledge / design, or mostly retrieval + assembly? ### 3. Record the outcome (functions: success + failure) When the task ends, record it — this is how the advisor learns. - **Success** (user satisfied / checks pass): `record --outcome success` — future similar tasks will get this combo first. - **Failure / dissatisfaction / quality drop**: `record --outcome fail --reason ""`, then diagnose with the table below. - Ambiguous: `partial`. ## Heuristics for cold start (no registry match) Reason from the task shape, not from fixed prices. | Task shape | Start with | Why | | --------------------------------------------------------------- | ---------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | | Formal, verifiable, high-volume (extract, reconcile, convert) | **economy tier · Medium** | Quality holds when the task is formal; only cost differs, so pick the cheapest that passes. | | Judgment / analysis, mid failure cost | **balanced tier · High** | Effort helps up to a point; the balanced tier is usually enough. | | Human-reviewed draft (slides, docs) | **economy tier · High → human finishes** | Cheap first pass, human does the last mile. | | Complex build, mid–high failure cost (apps, simulations, media) | **balanced tier · Max** | The balanced tier at high effort often matches the frontier tier's quality for far less; the frontier tier rarely justifies its cost here. | | Deep knowledge / physics / architecture | **frontier tier · High** | This is where model size genuinely decides the outcome. Stay at High. | | High failure cost / one correct answer (migration, security) | **frontier tier · High**, escalate to Max on failure | Buy a safety margin. Still not Ultra by default. | ## Effort has a ceiling — a core principle Reasoning effort shows **diminishing returns**, and past a point more effort can _hurt_: - **Over-fitting to the letter of the spec** instead of its intent — optimizing to pass the check rather than to do the work well (reward hacking / benchmark gaming). - **Runaway self-polishing** after the result is already acceptable — cost and wall-clock explode with no quality gain. - **Delegation overhead** without added insight. Consequences for choosing effort: - **High is usually the effective ceiling.** Going beyond it is often waste. - **Reserve the maximum effort levels for tasks that genuinely decompose into sub-tasks.** Otherwise they burn budget for nothing — verify before using them. - **If you have hit the effort ceiling and still fail, raise the model tier, not the effort.** ## Failure diagnosis — pick the right fix | Symptom | Likely cause | Fix | | ----------------------------------------------------------------- | ---------------------------- | ----------------------------------------------------------------- | | Wrong or shallow at low effort | not enough deliberation | **raise effort one step** (same tier first) | | **Still wrong at High effort** | intelligence ceiling reached | **raise the model tier — not the effort** | | Games the check / literal-but-wrong (worse the higher the effort) | over-optimization | **lower effort and sharpen the spec / strengthen the check** | | Passed but keeps polishing; time and cost balloon (top effort) | no stop condition | **drop to a bounded effort and state a clear Definition-of-Done** | | Quality is fine but too slow or expensive | over-provisioned | **lower the tier and/or effort** | | Delegates but no added insight | delegation overhead | **avoid the top effort; use a focused High run** | After suggesting a fix, record the retry's outcome to close the loop. ## Applying it in codex - Launch: `codex -m gpt-5.6- -c model_reasoning_effort=` (e.g. `-m gpt-5.6-terra -c model_reasoning_effort=high`). - Switch mid-session: `/model`. - Codex does not change the model per turn on its own. This skill recommends and records; the user makes the switch. ## Output format Keep it short (3–5 lines) and framed as a proposal, not a verdict: > **Recommend: ` · `** — (similar past success: ) > Run this first; if **** fails, escalate to **``**. > Launch: `codex -m gpt-5.6- -c model_reasoning_effort=` · current session: `` > I'll record the outcome so the next recommendation improves.