Gate Check
Identify pipeline decisions that don't need expensive models and propose cheaper alternatives.
Installation
- Make sure Claude is on your device and in your terminal.
Skills load from
~/.claude/skills/when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then runclaudein any terminal to verify.One-time setupnpm i -g @anthropic-ai/claude-codeAlready have it? Skip ahead.
- Paste into Claude Code or into your terminal.
This copies the whole skill folder into
~/.claude/skills/gate-check-undefined-ui/— the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.Faster alternative (instruction-only skills)
Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.
Quick install (SKILL.md only)Sign up to copy - Restart Claude Code.
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~/.claude/skills/). New skills are picked up on startup. - Just ask Claude.
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Prefer to read the source first? Open on GitHub.
When Claude uses it
Find the decisions in a pipeline that do not need the expensive model and propose the gate for each: a rule, a classic classifier, or a small model, with fail-closed routing. Use when the user asks to cut model costs or latency, says the big model handles everything, or wants a triage / routing / filter layer in front of an agent. Analysis plus an optional baseline scaffold. Do NOT use for auditing context layout (context-audit) or for building eval suites (evals-bootstrap).
What this skill does
Find the gates
Theory: Cheap decisions and Gate practice. An agent does two kinds of work: it writes, which needs a big model, and it decides — is this spam, which queue, does this need a person — which is a bounded question the answer to which comes from a set you already know. A gate is a cheap decision layer that sorts the stream so the expensive model only sees the items that actually need judgement.
Workflow
- Map the decisions. Read the pipeline's entry points and prompts and list every decision made before or during a model call: classification, routing, filtering, yes/no triage, priority, language, "is this even for us". Ignore the writing — only bounded decisions with a known label set.
- Count what each costs today. For each decision currently made by the big model: calls per day if known, tokens per call, and what a wrong answer costs. A decision worth one bit that burns a frontier call is the headline finding.
- Propose the cheapest gate that can hold it, in rising order of cost:
- a rule — regex, allowlist, header check: free, instant, blind to anything unanticipated; always the first layer, never the last
- a classic classifier — logistic regression or similar over simple features, trained on a few hundred labelled examples: milliseconds, fractions of a cent, and the baseline every fancier option must beat
- a small / System One model — when the input is too varied for features but the output is still a label with a confidence score
- Route fail-closed. Every gate needs a confidence threshold, and doubt goes down the safe path: unsure means escalate to the big model (or a person), never means guess. Say explicitly what each gate's unsure route is.
- Offer the baseline scaffold. If the user wants to proceed, generate
the module's thirty-minute exercise for their data: a
label.pythat samples ~200 real examples for hand-labelling, and abaseline.pythat trains the classic classifier and prints accuracy against a held-out split. Every vendor claim and small-model option must beat this number on their data before it earns a place in the pipeline.
Output format
Gate check — <pipeline>
decisions found: <n>, currently on the big model: <n>
1. <decision> — <where in the code>
today: <who decides, est. cost> label set: <the labels>
gate: <rule | classifier | small model> — <why this tier>
unsure -> <escalation path>
saves: <est. calls/tokens diverted>
...
Rank by savings. If a decision genuinely needs the big model — open-ended, no stable label set, wrong answers are cheap to fix — say so and leave it alone; a gate that guesses is worse than no gate.
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