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 weiyi88/cc-code --skill deploy-vercel-supabasegit clone --depth 1 https://github.com/weiyi88/cc-codeWrote 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/weiyi88/cc-code/deploy-vercel-supabase)<a href="https://agentmods.dev/skills/weiyi88/cc-code/deploy-vercel-supabase"><img src="https://agentmods.dev/badge/skills/weiyi88/cc-code/deploy-vercel-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/weiyi88/cc-code/deploy-vercel-supabase"><img src="https://agentmods.dev/badge/skills/weiyi88/cc-code/deploy-vercel-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 | $0.00016 | $0.01505 |
| Opus 5.5 | $0.00006 | $0.00602 |
| Sonnet 5.5 | $0.00003 | $0.00301 |
| Haiku 4.5 | $0.00002 | $0.00151 |
Grade A, and why
deploy-vercel-supabase scanned grade A with 2 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
**不能用直连**(中国网络 DNS 解析失败),必须用 REST API + curl。 Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run([ How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vercel + Supabase 一键部署
前置条件(用户需提前完成)
- 本地
.env已配置(我直接读取,无需用户手动提供) - Supabase Access Token(
sbp_开头),用户在对话中提供 - GitHub 远程仓库已配置(
git remote -v可见) - Vercel CLI 已本地登录(
vercel whoami可用) - GitHub OAuth App Callback URL 已改为新生产域名(唯一需要用户手动操作的步骤)
部署流程
Step 1:读取环境配置
cat .env
从 .env 提取:
DATABASE_URL(Supabase Transaction Pooler,端口 6543)DIRECT_URL(Supabase Direct Connection,端口 5432)NEXTAUTH_URL(生产域名)- 其他所有变量
从 DIRECT_URL 解析出 Supabase Project Ref(格式:db.<ref>.supabase.co)
Step 2:提取本地 PostgreSQL Schema
pg_dump -h localhost -U <本地DB用户> -d <本地DB名> \
--schema-only --no-owner --no-acl \
-f .cc_code/scripts/schema_export.sql
注意:用户名不是
postgres,是本地系统用户(如blue_focus)。如果失败,用psql -l查看实际用户。
Step 3:通过 Supabase Management API 导入 Schema
不能用直连(中国网络 DNS 解析失败),必须用 REST API + curl。
import subprocess, re
access_token = "<用户提供的 sbp_ token>"
project_ref = "<从 DIRECT_URL 解析的 ref>"
with open(".cc_code/scripts/schema_export.sql") as f:
sql_content = f.read()
# 逐行状态机解析 SQL 语句(处理多行 + dollar-quoting)
statements = parse_sql_statements(sql_content)
for i, stmt in enumerate(statements):
payload = {"query": stmt}
result = subprocess.run([
"curl", "-s", "-X", "POST",
f"https://api.supabase.com/v1/projects/{project_ref}/database/query",
"-H", f"Authorization: Bearer {access_token}",
"-H", "Content-Type: application/json",
"-d", json.dumps(payload)
], capture_output=True, text=True)
print(f"[{i+1}/{len(statements)}] {'✓' if '\"error\"' not in result.stdout else '✗'}")
SQL 解析规则(状态机,非正则):
- 遇到
$$进入 dollar-quote 模式,直到下一个$$退出 - 遇到
'进入字符串模式,''是转义不退出 - 遇到
--跳过行注释 ;在普通模式下才是语句分隔符
Step 4:推送代码到 GitHub
git add -A
git commit -m "feat(deploy): deploy to Vercel + Supabase"
git push origin main
Step 5:配置 Vercel 环境变量
# 检查项目是否已关联
vercel ls
# 如果是新项目,先关联
vercel link
# 批量设置环境变量(从 .env 读取)
vercel env add DATABASE_URL production
vercel env add DIRECT_URL production
vercel env add AUTH_SECRET production
vercel env add AUTH_GITHUB_ID production
vercel env add AUTH_GITHUB_SECRET production
vercel env add SMTP_USER production
vercel env add SMTP_PASS production
vercel env add SMTP_FROM production
vercel env add NEXTAUTH_URL production
vercel env add THIRD_PARTY_AI_KEY production
vercel env add THIRD_PARTY_AI_BASE_URL production
vercel env add THIRD_PARTY_AI_MODEL production
What ships with it
1 file 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 Changed · -68 tokens per session 25241039782b
- 1mo ago First seen · 193 lines · 84 tokens per session scan A 1eda7d1ca104
deploy-vercel-supabase is a skill published in the GitHub repository weiyi88/cc-code (5 stars, last pushed yesterday), licensed MIT. It adds 16 tokens to every session and 1,505 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-13.
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