Model-to-task fit across every call site — where cheaper and faster would do, where quality is starved, and what happens when a provider blinks.
Free & open · no signup · read-only — it ends by asking · nothing leaves your machine
You are working inside this repo. Mission: review every model call site for fit — the right model, settings, and fallback for each job — and find where the product overpays, underserves, or has no plan B.
Read-only pass. Your only write is the report file.
Phase 2 sweeps the codebase through every one of these, citing file and line for each finding.
frontier models doing classification and extraction a small model handles; small models visibly failing reasoning-heavy jobs (check traces and complaints)
floating aliases that drift under you vs pinned versions aging toward deprecation; is there a policy at all
provider outage or rate-limit equals product outage; no second model, no queue, no graceful message
temperatures nobody chose, max_tokens silently truncating outputs, defaults everywhere
parsing prose with regex where JSON mode or tool calling exists
provider prompt caching and response memoization unused on repeated prefixes and idempotent calls
how a new model version gets evaluated before swap; ad hoc vibes or an eval run (see 34)
One structured report at the repo root — or in reports/, if you keep one — the same shape every time, ready for a teammate — or the optional Studio — to act on.
Every Goal Prompt follows the same four steps, so results are consistent and repeatable — no matter which one you run.
Every place a model is invoked.
Fit — frontier models doing classification and extraction a small model handles.
Every swap candidate carries expected deltas.
Create MODELS.md at repo root.
Copy it and paste it into your agent inside the repo you want checked.
Install the goal plugin once — two commands — then just type /goal:model-strategy-review.
/plugin marketplace add GhostlyGawd/goal-prompts/plugin install goal@goal-promptsOr install only this Goal Prompt as /goal-model-strategy-review:
curl -fsSL https://goal-prompts.vercel.app/install | BRIEF=36 shLet an agent fetch it mid-conversation, or pull the raw Goal Prompt by URL.
https://goal-prompts.vercel.app/raw/36.mdNothing hidden — this is the whole Goal Prompt, verbatim. Read it in a minute, edit it, or copy it as-is.
# Goal: Model Strategy Review You are working inside this repo. Mission: review every model call site for fit — the right model, settings, and fallback for each job — and find where the product overpays, underserves, or has no plan B. Read-only pass. Your only write is the report file. ## Phase 1 — Inventory the call sites - Every place a model is invoked: model name, parameters, purpose, estimated volume, latency tolerance. - How are models referenced — pinned versions, floating "latest", env config? - What happens today if the primary provider errors or stalls? Trace it. ## Phase 2 — Audit through 7 lenses 1. **Fit** — frontier models doing classification and extraction a small model handles; small models visibly failing reasoning-heavy jobs (check traces and complaints) 2. **Pinning risk** — floating aliases that drift under you vs pinned versions aging toward deprecation; is there a policy at all 3. **Fallback absence** — provider outage or rate-limit equals product outage; no second model, no queue, no graceful message 4. **Parameter hygiene** — temperatures nobody chose, max_tokens silently truncating outputs, defaults everywhere 5. **Structured output** — parsing prose with regex where JSON mode or tool calling exists 6. **Caching leverage** — provider prompt caching and response memoization unused on repeated prefixes and idempotent calls 7. **Upgrade process** — how a new model version gets evaluated before swap; ad hoc vibes or an eval run (see 34) ## Phase 3 — Curate - Every swap candidate carries expected deltas: quality risk, latency, cost per call × volume - Resilience gaps ranked by user-facing blast radius ## Phase 4 — Report Create `MODELS.md` at repo root: 1. **Call-site table** — site · model · params · volume · latency need · verdict 2. **Swap candidates** — each: current → proposed · expected deltas · eval to run first 3. **Resilience plan** — fallback chain, timeout, and user-facing behavior per failure mode 4. **Pinning policy** — one paragraph the team can adopt 5. **Quick wins** — parameter and caching fixes shippable today Start the report with today's date. If `MODELS.md` already exists from a previous run, read it first and lead with what changed since. ## Rules - No model swap without an eval gate; cost savings that ship regressions are debts - Match the model to the step, not the brand to the product - No model calls in this repo? Say so in a one-paragraph null report and stop — a null result is a valid finding. - If a `reports/` directory exists at the repo root, write the report there instead of the root. - Before asking, present the top findings as a ranked list in plain words - Report only — end by asking which changes to make
reports/ directory exists at the repo root, write the report there instead of the root.Every system prompt in the repo: contradictions, dead instructions, bloat, and injection surface — with a consolidation plan.
Your tools are an API whose consumer is a model — audit names, descriptions, error contracts, and guards on destructive actions.
How runs end — iteration caps, stop conditions, stuck-state detection, and the true cost of one pathological run.
Reconstruct what actually enters the model's window each step — stale payloads, token hogs, retrieval junk, and buried instructions.
Runs inside these playbooks — curated sequences you can launch with one paste: