Borrowing it
Nothing to install: this file belongs to emilyLi2020/WAVE. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/emilyLi2020/WAVE/main/.agents/skills/supabase/SKILL.mdgit clone --depth 1 https://github.com/emilyLi2020/WAVEWrote 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/emilyli2020/wave/supabase)<a href="https://agentmods.dev/skills/emilyli2020/wave/supabase"><img src="https://agentmods.dev/badge/skills/emilyli2020/wave/supabase/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/emilyli2020/wave/supabase"><img src="https://agentmods.dev/badge/skills/emilyli2020/wave/supabase.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 | $0.00125 | $0.01768 |
| Opus 5.5 | $0.00050 | $0.00707 |
| Sonnet 5.5 | $0.00025 | $0.00354 |
| Haiku 4.5 | $0.00013 | $0.00177 |
Grade A, and why
supabase scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
`curl -so /dev/null -w "%{http_code}" https://mcp.supabase.com/mcp` This is a copy
83% identical to supabase — 85 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Supabase
Core Principles
1. Supabase changes frequently — verify against current docs before implementing. Do not rely on training data for Supabase features. Function signatures, config.toml settings, and API conventions change between versions. Before implementing, look up the relevant topic using the documentation access methods below.
2. Verify your work. After implementing any fix, run a test query to confirm the change works. A fix without verification is incomplete.
3. Recover from errors, don't loop. If an approach fails after 2-3 attempts, stop and reconsider. Try a different method, check documentation, inspect the error more carefully, and review relevant logs when available. Supabase issues are not always solved by retrying the same command, and the answer is not always in the logs, but logs are often worth checking before proceeding.
4. RLS by default in exposed schemas.
Enable RLS on every table in any exposed schema, especially public. This is critical in Supabase because tables in exposed schemas can be reachable through the Data API. For private schemas, prefer RLS as defense in depth. After enabling RLS, create policies that match the actual access model rather than defaulting every table to the same auth.uid() pattern.
5. Security checklist. When working on any Supabase task that touches auth, RLS, views, storage, or user data, run through this checklist. These are Supabase-specific security traps that silently create vulnerabilities:
- Auth and session security
- Never use
user_metadataclaims in JWT-based authorization decisions. In Supabase,raw_user_meta_datais user-editable and can appear inauth.jwt(), so it is unsafe for RLS policies or any other authorization logic. Store authorization data inraw_app_meta_data/app_metadatainstead. - Deleting a user does not invalidate existing access tokens. Sign out or revoke sessions first, keep JWT expiry short for sensitive apps, and for strict guarantees validate
session_idagainstauth.sessionson sensitive operations. - If you use
app_metadataorauth.jwt()for authorization, remember JWT claims are not always fresh until the user's token is refreshed.
- Never use
What ships with it
2 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.
- 1mo ago First seen · 105 lines · 125 tokens per session scan A 3c3f8be0662a
supabase is a skill published in the GitHub repository emilyLi2020/WAVE (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 125 tokens to every session and 1,768 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 83% identical to supabase, differing in 85 lines, and is treated as a copy.
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