Software Development
Model Evaluation Harness
Generates a full ML model evaluation framework with task-appropriate metrics, evaluation datasets, baseline comparisons, and a populated model card. Useful for making ML evaluation a default step instead of a skipped one. ML engineers shipping classifiers, regressors, or generative models; AI engineers shipping LLM-backed features; applied researchers who need a consistent evaluation story across experiments. Teams ship models on vibes, and the production regression shows up weeks later as a support ticket, a churn spike, or an angry Slack from sales. The reasons evaluation gets skipped are mundane: choosing the right metrics takes expertise, assembling an evaluation dataset is tedious, statistical significance is easy to get wrong, and model cards live in a drawer. A structured harness flips that — evaluation becomes a `pnpm eval` command with a results summary that pastes into a PR.
One-Time Purchase
$19.99
Model Evaluation Harness — Support Triage Classifier at FinTrack
Task: Multiclass classification — 5 classes (Billing, Technical, Account, Integration, General)
Candidate model: triage-v3 — gpt-4o-mini with optimized prompt, structured output
Compared against: triage-v2 (current prod, gpt-4o), majority baseline, Claude Sonnet
Eval dataset: 1,200 held-out tickets from the past 90 days, stratified across classes, double-labeled with adjudication.
Summary
Headline
triage-v3 (gpt-4o-mini) is the recommended winner. It matches the current production model on macro-F1 within margin, runs about 4.2x cheaper, and has lower tail latency. Claude Sonnet is the highest-accuracy model but is two times more expensive than the current prod and three times more expensive than the recommended winner — the accuracy lift does not pay for the cost lift at FinTrack's volume.
Cost-per-1K-calls drops by 76% with no statistically significant accuracy loss vs the current prod model. Estimated monthly savings at 500K calls: ~$320.
Model Comparison
| Model | Macro-F1 | Accuracy | Cost / 1K calls | p95 latency | Verdict |
|---|---|---|---|---|---|
| Majority baseline | 0.18 | 0.34 | $0.00 | n/a | Baseline |
triage-v2 — gpt-4o (current prod) | 0.89 | 91.0% | $0.84 | 1.4s | Holdable |
triage-v3 — gpt-4o-mini (candidate) | 0.88 | 90.4% | $0.20 | 0.9s | Recommended |
| Claude Sonnet 4.5 | 0.92 | 93.1% | $1.78 | 1.6s | Premium |
Per-Suite Scores
Correctness (macro-F1 on holdout)
Latency (p95, end-to-end including network)
Cost per 1K calls (input + output)
Per-Class Breakdown (triage-v3 vs triage-v2)
| Class | F1 (v2) | F1 (v3) | Δ | Notes |
|---|---|---|---|---|
| Billing | 0.92 | 0.91 | −0.01 | Within margin |
| Technical | 0.90 | 0.89 | −0.01 | Within margin |
| Account | 0.91 | 0.92 | +0.01 | Within margin |
| Integration | 0.85 | 0.81 | −0.04 | Regression |
| General | 0.87 | 0.86 | −0.01 | Within margin |
Statistical Significance
Test setup
Paired bootstrap (10,000 resamples) on macro-F1. Null: triage-v3 is no worse than triage-v2 by more than 1pp. p-value on the overall F1 difference: 0.31 — not significant at α = 0.05. The Integration-class regression is significant at p = 0.04 and worth a closer look before ship.
Verdict
Recommended: ship triage-v3 with a guardrail
Ship gpt-4o-mini as the new production model behind feature flag triage_model_v3. The 4.2x cost reduction is real; the accuracy delta is within margin overall. Route Integration-class tickets to a fallback (re-classify with gpt-4o, or escalate to a human) until the regression is addressed.
Not recommended: Claude Sonnet 4.5
Sonnet is the strongest model on raw accuracy but the cost increase outweighs the lift at FinTrack's volume. Revisit if customer-facing classification errors start driving CSAT issues or if Anthropic ships a tier-pricing change. The 1pp accuracy lift over gpt-4o costs ~$470/month at current volume.
Regression Risk
Integration-class regression
The Integration class F1 drops 4 points on the smaller model. Inspection of the 48 misclassified tickets shows the model struggles when the ticket mentions a third-party tool name without explicit context ("Zapier broke" classified as Technical instead of Integration). Two mitigations: (1) add a few-shot example covering bare tool mentions, (2) post-classification rule that bumps tickets matching a known integration tool list to Integration with 0.6 confidence.
Distribution drift watch
The eval holdout is from the past 90 days. If ticket distribution shifts (e.g., a new integration partner launches), the model's calibration on the affected class will drift first. Run this eval monthly against a fresh holdout for the first quarter post-launch.
Model Card (excerpt)
- Intended use: Routing inbound support tickets to one of five categories at FinTrack Platform.
- Out of scope: PII redaction, sentiment scoring, priority assignment.
- Training data: None — zero-shot with a structured prompt and 3 few-shot examples per class.
- Known failure modes: Bare third-party tool mentions, non-English tickets, tickets under 8 words.
- Owner: ML Platform team. On-call rotation: pager-app group
ml-triage.
This sample illustrates the skill's output format. FinTrack Platform is a fictional company used recurringly across these sample outputs. Real eval results are never included in sample outputs.
View full sample →
All sales final. No refunds on digital products.
Includes support for Claude Code, Codex, OpenClaw, and Google Antigravity in the same license.
Also in AI Engineering
Bundle price: $55. Compare this skill with the full workflow bundle or Pro access.
Best for
ML and AI engineers shipping classifiers, regressors, or LLM-backed features who keep skipping evaluation because choosing metrics, assembling datasets, and getting statistical significance right is tedious. Most useful when the team commits to running the harness on every model version before deploy and the output flows into a PR summary.
Not ideal for
Pure exploratory research where the eval shape is still being discovered and a generic harness would lock in the wrong metrics early. Also a poor fit for production-safety evaluations (medical diagnostic models, credit decisions, safety-critical control) where the evaluation methodology must be validated against the regulatory regime, not the skill’s defaults.
Included in this purchase
- Claude Code, Codex, OpenClaw, and Google Antigravity skill files.
- Setup guidance for the right adapter in your workspace.
- One-time license for the purchased skill version.
Setup
Plan for a short setup in the repository or workspace where the skill will run. Some coding familiarity helps for implementation-heavy outputs.
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Future Updates
This purchase includes the current version of the skill. If you want future adapter updates — meaning compatibility and packaging updates as supported platforms evolve — plus new catalog additions included automatically, upgrade to Pro.