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 ericrisco/rsc-harness --skill neongit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/neon)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/neon"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/neon/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/ericrisco/rsc-harness/neon"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/neon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00093 | $0.03510 |
| Opus 5 | $0.00046 | $0.01755 |
| Sonnet 5 | $0.00019 | $0.00702 |
| Haiku 4.5 | $0.00009 | $0.00351 |
Grade A, and why
neon 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 5d 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neon — serverless Postgres as a platform
Neon is Postgres with three platform features the engine doesn't have: a serverless driver that
talks over HTTP/WebSocket (so you can query from edge runtimes), built-in connection pooling, and
copy-on-write branching that makes a fork of production data appear instantly. This skill owns
that platform layer. The Postgres engine underneath — schema, indexes, EXPLAIN, RLS, zero-downtime
DDL — is identical to any Postgres 16 and belongs to ../postgresdb/SKILL.md. Don't re-derive engine
craft here; connect correctly and branch correctly.
When to use / When NOT to use
When to use:
- Code imports
@neondatabase/serverless, orDATABASE_URLpoints at*.neon.tech/*.aws.neon.tech. - Connecting from Vercel (Edge/Node), Cloudflare Workers, AWS Lambda, or any serverless/edge function — choosing HTTP
neon()vs WebSocketPool. - Setting up branching: dev branch off production, a branch per PR in CI,
neonctlusage, the Neon + Vercel preview integration. - Neon-specific symptoms: cold start after scale-to-zero, "too many connections" despite being serverless, pooled-vs-direct string confusion, autoscaling CU sizing/cost.
- Picking the connection string for migrations (direct) vs app queries (pooled).
When NOT to use — route to the sibling:
- Schema design, index choice, EXPLAIN/ANALYZE, query tuning, RLS, VACUUM, partitioning, expand-contract migrations →
../postgresdb/SKILL.md. Neon adds nothing to the engine; defer. - Drizzle schema/migrations →
../drizzle-orm/SKILL.md; Prisma →prisma-orm. Neon supplies only the driver adapter line. - A different managed Postgres/BaaS: Supabase (auth + storage + realtime) →
supabase; PlanetScale →planetscale; Turso/libSQL →sqlite-turso. - Where to deploy the app talking to Neon →
../vercel/SKILL.md/cloudflare/railway. - Backup/PITR strategy as a discipline →
backups(Neon branch-as-restore is mentioned here, not owned).
Non-negotiables
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.
- 5d ago First seen · 219 lines · 93 tokens per session scan A 1e9f63b2d649
neon is a skill published in the GitHub repository ericrisco/rsc-harness (70 stars, last pushed yesterday), licensed MIT. It adds 93 tokens to every session and 3,510 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 2026-09-03.
Other skills, from other repositories
dev-supabase
Backend development with Supabase. Trigger when the user wants to configure auth, the database, or Supabase storage.
ops-database
Database schema design. Trigger when the user wants to create tables, migrations, or optimize queries.
traverse-multi-hop
Expresses a multi-hop lineage question as a single variable-length path match against native graph storage, bounded by an explicit hop depth and an explicit relationship-type allowlist, instead of a recursive relational join that grows one level per hop.
attach-receipts
Attaches sourcedoc/extractionrunid/schemaversion receipts to every edge at write time, and refuses to write any edge missing one of the three.
query-graph
Loads schema.sql into a local SQLite file, then answers availability and provenance questions against the nodes/edges tables with real SQL instead of re-reading source material.
score-and-merge
Scores candidate merge pairs on weighted signals and auto-merges only pairs clearing a stated threshold, routing the rest to a review queue with their score and signal breakdown attached.