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 RBraga01/builder-ai --skill eval-before-shipgit clone --depth 1 https://github.com/RBraga01/builder-aiWrote 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/rbraga01/builder-ai/eval-before-ship)<a href="https://agentmods.dev/skills/rbraga01/builder-ai/eval-before-ship"><img src="https://agentmods.dev/badge/skills/rbraga01/builder-ai/eval-before-ship/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/rbraga01/builder-ai/eval-before-ship"><img src="https://agentmods.dev/badge/skills/rbraga01/builder-ai/eval-before-ship.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 | $0.00046 | $0.01357 |
| Opus 5.5 | $0.00018 | $0.00543 |
| Sonnet 5.5 | $0.00009 | $0.00271 |
| Haiku 4.5 | $0.00005 | $0.00136 |
Grade A, and why
eval-before-ship 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.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Before Ship
The Law
AN LLM FEATURE IS NOT READY UNTIL NUMBERS EXIST.
"It looked good when I tested it" is not an eval.
"I ran a few examples and it worked" is not an eval.
A named suite, a defined metric, a pass rate, a failure analysis,
and a baseline comparison IS an eval. All five. Not four.
When to Use
Trigger before any of these:
- Merging a PR that adds or modifies a prompt
- Deploying an LLM feature to any environment users can reach
- Switching models or providers on an existing feature
- Changing retrieval logic, rerankers, or chunk strategy in a RAG pipeline
- Updating few-shot examples or system prompt structure
When NOT to Use
- Exploratory prototypes that will not reach users (note it: "eval required before production")
- Config-only changes that cannot affect model output (timeouts, logging, env vars)
What Counts as an Eval
An eval must have all five components:
| Component | What It Means | What Does NOT Count |
|---|---|---|
| Named test suite | File with labelled examples in evals/ |
"I tested it manually" |
| Defined metric | Accuracy %, faithfulness score, task pass rate | "It seemed accurate" |
| Pass threshold | Explicit minimum (e.g., ≥ 85%) | No threshold = no standard |
| Failure analysis | ≥ 5 failures examined and categorised | "There were a few errors" |
| Baseline comparison | This version vs. previous version or control | First release exempt; all subsequent require it |
The Process
Step 1 — Define the Eval Before Writing the Prompt
Answer these before touching the prompt:
- What does a correct output look like for this task?
- What metric will you use to measure it?
- What is the minimum acceptable score for production?
If you cannot answer these before building, the task is not well enough specified to build.
Step 2 — Build a Representative Test Set
evals/
<feature-name>/
test-set.jsonl ← labelled examples, one JSON object per line
harness.py ← eval runner
results-<date>.md ← documented results
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 · 141 lines · 46 tokens per session scan A 7e1f6ffe5219
eval-before-ship is a skill published in the GitHub repository RBraga01/builder-ai (2 stars, last pushed 10d ago), licensed MIT. It adds 46 tokens to every session and 1,357 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
ai-prompt-regression-testing
Use when comparing prompt changes against a fixed evaluation set: record actual model settings, required checks, missing results, judge limitations and case-level regression evidence.
recipe-eval-prompt
Compares original and optimized prompts through repeated blind paired execution in git worktrees. Use when evaluating prompt improvement effects or learning prompt engineering through concrete examples.
llm-evaluation
LLM evaluation — automated metrics, human feedback, benchmarking. Use when testing performance, measuring AI quality, or establishing evaluation frameworks.
fetch-llm-papers
Refresh the LLM landscape paper pool (section/xllmpapers.md) with fetchllmpapers.py. USE FOR: refreshing citation counts or expanding topic coverage. DO NOT USE FOR: hand-curated entries (use add-new-entry-from-temp-md) or bestpractices.md citation counts (use update-cite-count).
ai-prompt-regression-testing
Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.
ai-prompt-regression-testing
Use when comparing prompt changes against a fixed evaluation set: record actual model settings, required checks, missing results, judge limitations and case-level regression evidence.