adversarial-tanstack

adversarial-tanstack is a skill for Claude Code, Codex from pproenca/dot-skills. It costs 143 tokens per session (1,326 once invoked), scanned A, original, MIT.

A pass/fail review gate for TanStack Start and TypeScript web applications. TanStack Start is a web framework; the gate checks client/server boundaries, authentication, server functions, server routes, and server-side rendering against fixed rules.

In plain words
What is it for?
Use it to review TanStack Start routes, server functions, authentication changes, environment variables, and server-rendered data loading.
Why use it?
It helps catch changes that expose secrets, return data to unauthorized users, load data on the wrong side of the app, or rely on outdated framework APIs before merging.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents; mentions Codex.

Good fit Use it to review TanStack Start routes, server functions, authentication changes, environment variables, and server-rendered data loading.

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Install with agentmods
npx agentmods add skills/pproenca/dot-skills/adversarial-tanstack
Install

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.

Any agent
npx skills add pproenca/dot-skills --skill adversarial-tanstack
Clone the repo
git clone --depth 1 https://github.com/pproenca/dot-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for adversarial-tanstack

README.md
[![agentmods](https://agentmods.dev/badge/skills/pproenca/dot-skills/adversarial-tanstack/github.svg)](https://agentmods.dev/skills/pproenca/dot-skills/adversarial-tanstack)
Your own site
<a href="https://agentmods.dev/skills/pproenca/dot-skills/adversarial-tanstack"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/adversarial-tanstack/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.

agentmods 80×15 button for adversarial-tanstack

Your own site · 80×15
<a href="https://agentmods.dev/skills/pproenca/dot-skills/adversarial-tanstack"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/adversarial-tanstack.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 143 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,326 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00143 $0.01326
Opus 5 $0.00072 $0.00663
Sonnet 5 $0.00029 $0.00265
Haiku 4.5 $0.00014 $0.00133

Measured 5d ago against content hash b083dce57430, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

adversarial-tanstack 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.

skills/.experimental/adversarial-tanstack/SKILL.md · 57 lines

How it starts

The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Adversarial TanStack Gate

A merge gate for TanStack Start + TypeScript web-app changes — a pass/fail gate: a single blind reviewer subagent judges the work against this gate's rules with an adversarial mandate, and the work passes only when every rule is PASS or N/A. This skill renders verdicts; it never fixes the work.

Rules are pinned to @tanstack/react-start 1.168+ (v1 RC, July 2026) and TypeScript 5.8–6.x semantics.

When to Apply

  • A TanStack Start feature, route, or server function is about to merge and needs an objective PASS/FAIL, not advisory feedback.
  • An agent (Claude, Codex) authored Start app code and you want an independent check its author bias cannot rubber-stamp.
  • A diff touches the client/server boundary — env vars, loaders, server functions, auth — where a wrong PASS ships secrets or unauthorized data access.
  • Auditing an existing Start codebase file set against the current v1 RC API surface (stale .inputValidator(), removed createServerFileRoute).

Do not apply to non-Start React apps (most serverfn/boundary/ssr rules will return N/A and the gate degenerates to a TypeScript check) or when the user wants explanations and refactors rather than a verdict.

Review Protocol

Follow these steps exactly — the gate's value is that every review runs the same way.

  1. Identify the target. Pin down exactly what is under review (a diff, a set of files, a PR) and note the ref/paths so the review runs against an unambiguous, fixed target. Always include tsconfig.json, src/router.tsx, and src/start.ts (if present) in the target paths — several rules are decided by those files even when the diff does not touch them.
  2. Load the rules. Read references/_sections.md and every rule file in references/ (all boundary-*.md, serverfn-*.md, sec-*.md, ssr-*.md, types-*.md, tscfg-*.md files).
  3. Compose the reviewer prompt. Fill references/reviewer-prompt.md with the rules and the target. The composed prompt must be fully self-contained — a reviewer sees no conversation history, so nothing may refer to context outside the prompt.
  4. Dispatch one blind reviewer. Launch a single Task subagent whose entire input is the composed prompt — no conversation context, no commentary alongside it.
  5. Render fail-closed. The reviewer's structured output is the verdict — there is no merge step. Overall verdict is PASS only when every rule is PASS or N/A; any single FAIL fails the gate. Never average, weigh severity, or waive a rule — a "minor" FAIL is a FAIL.
  6. Render the verdict. Fill assets/templates/verdict.md. On FAIL, aggregate the reviewer's "missing for PASS" suggestions into the fix list, each with its location, ordered by category importance. Every rule whose final result is FAIL must appear in the fix list with a change concrete enough to apply as written — if the reviewer's suggestion only restates the violation, derive the fix from the rule's Correct example before rendering.

Read the full file on GitHub · 57 lines

Changes

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.

  1. 5d ago First seen · 57 lines · 143 tokens per session scan A b083dce57430

Subscribe to this mod's changes

adversarial-tanstack is a skill published in the GitHub repository pproenca/dot-skills (205 stars, last pushed 24d ago), licensed MIT. It adds 143 tokens to every session and 1,326 once invoked, about $0.0007 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.