Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ishandutta2007/Awesome-Agent-Skills --skill eval-driven-developmentgit clone --depth 1 https://github.com/ishandutta2007/Awesome-Agent-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ishandutta2007/awesome-agent-skills/eval-driven-development)<a href="https://agentmods.dev/skills/ishandutta2007/awesome-agent-skills/eval-driven-development"><img src="https://agentmods.dev/badge/skills/ishandutta2007/awesome-agent-skills/eval-driven-development/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ishandutta2007/awesome-agent-skills/eval-driven-development"><img src="https://agentmods.dev/badge/skills/ishandutta2007/awesome-agent-skills/eval-driven-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.00402 |
| Opus 5.5 | $0.00011 | $0.00161 |
| Sonnet 5.5 | $0.00006 | $0.00080 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
eval-driven-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Eval-Driven Development
Overview
Eval-Driven Development ensures that AI features behave deterministically and predictably by testing them against a golden dataset using automated evaluators.
When to Use
- Building an AI-powered feature
- Tuning prompts or changing underlying models
- Implementing RAG pipelines
Process
- Curate Golden Dataset: Create diverse test cases including edge cases.
- Define Metrics: Choose appropriate evaluators (e.g., exact match, semantic similarity, LLM-as-a-judge).
- Run Pipeline: Execute the AI feature over the dataset and collect results.
- Analyze Failures: Inspect low-scoring examples and update prompts or logic.
- Establish Baseline: Set a minimum threshold for CI/CD checks.
Common Rationalizations
| Rationalization | Why It Is Wrong |
|---|---|
| "Manual spot checks are enough." | Spot checks miss regressions across prompts, model versions, and edge cases. |
| "We can add evals after launch." | Without a baseline, you cannot tell whether a later prompt or model change improved behavior. |
| "The judge model says it is good." | LLM judges need criteria, calibration examples, and failure review before they are trustworthy. |
Red Flags
- No golden dataset exists
- Metrics are vague or not tied to user-visible quality
- Low-scoring examples are ignored instead of inspected
- The baseline threshold is chosen after seeing the desired result
Verification
Before finishing, confirm:
- The golden dataset includes normal, edge, and known-failure cases
- Metrics and evaluator prompts are committed or otherwise reproducible
- The current model/prompt has a recorded baseline
- Failure examples have been reviewed and categorized
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 46 lines · 28 tokens per session scan A 6e86feb37e2d
eval-driven-development is a skill published in the GitHub repository ishandutta2007/Awesome-Agent-Skills (21 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 402 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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