Supabase Evals is a test suite for measuring how well AI agents perform tasks involving Supabase, using defined scenarios, scorers, starting states, agent runtimes, and model experiments. It is for comparing agent and runtime configurations across local or hosted Supabase-like environments and recording their results. The catalogue add-ons support working with these evaluations.
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 supabase/evals --skill docs-eval-planninggit clone --depth 1 https://github.com/supabase/evalsWrote 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/supabase/evals/docs-eval-planning)<a href="https://agentmods.dev/skills/supabase/evals/docs-eval-planning"><img src="https://agentmods.dev/badge/skills/supabase/evals/docs-eval-planning/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/supabase/evals/docs-eval-planning"><img src="https://agentmods.dev/badge/skills/supabase/evals/docs-eval-planning.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 | $0.00092 | $0.02611 |
| Opus 5 | $0.00046 | $0.01306 |
| Sonnet 5 | $0.00018 | $0.00522 |
| Haiku 4.5 | $0.00009 | $0.00261 |
Grade A, and why
docs-eval-planning 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 today.
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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planning a documentation eval
A documentation eval measures a page, not an agent. The prompt sends a user's request plus the page's url, and the checks say whether an agent that read the page produced working code. A gap in the page counts as a failure.
That framing is the whole difficulty. The failure modes are collected in references/flakiness.md. Read it before designing checks, not after.
What this skill produces
A plan. It does not write PROMPT.md, EVAL.ts, or a seed. Implementation and local scoring are a
separate job.
The shape it takes
Model a new eval on build-docs-003-api-keys-guide or later.
build-docs-NNN-<subject>, named after the doc rather than the feature. The id is permanent. The
published results series is keyed on it, so renaming breaks history. So does renaming a check.
evals/docs/build-docs-NNN-<subject>/
PROMPT.md frontmatter and the task the agent sees
EVAL.ts the scorer
README.md the design rationale, addressed to the next editor
<helper>.ts flat beside EVAL.ts, never in a subdirectory
local/ the seed workspace
CONTRIBUTING.md makes the README.md optional. For a page under test it is required: it is the only
place the stripped-word list and the constraints that must not be edited away live. Its sections:
- What this eval measures. The page is the subject, not the agent.
- Do not reintroduce the vocabulary. The stripped words, listed.
- The seed carries the contract. What the seed fixes, and what each fixed thing buys and costs.
- Do not drop the positive controls. Which checks pass for an agent that built nothing, and which ones make them mean something.
- The guide has to actually be read.
- What this eval does not score. Each entry with its reason. An unmeasured rule from the prompt is named here.
EVAL.ts is orchestration. Import named check* functions, assemble one flat array, return
{ passed: checks.every(c => c.passed), checks }, and wrap it in a try/catch whose catch returns a
single self-named failure check. Split the implementations into modules and keep the full check list
declared in EVAL.ts. Comment why the phase order is what it is.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today First seen · 206 lines · 92 tokens per session scan A 89010d288dbf
docs-eval-planning is a skill published in the GitHub repository supabase/evals (136 stars, last pushed today), licensed Apache-2.0. It adds 92 tokens to every session and 2,611 once invoked, about $0.0005 per session on Opus 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 2026-09-18.
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