Claude Skill MartBrowse skillsQuick linesLearn by videoTerminal guideWhat is a Skill?
← Back to list

Gate Check — LLM Cost Triage Analysis

Finds pipeline decisions that don't need a frontier model and proposes the cheapest gate — rule, classic classifier, or small model — with fail-closed routing.

Dev & CodingIntermediate★ 815⑂ 128AI score 8/10Last updated: Sep 29, 2026

What it does

  • Reads your pipeline's entry points and prompts, then lists every bounded decision: classification, routing, filtering, yes/no triage, priority, language, "is this even for us".
  • Estimates today's cost per decision (calls/day, tokens per call, cost of a wrong answer) to surface one-bit questions burning frontier calls.
  • Proposes the cheapest gate that can hold each decision, in rising order: rule (regex, allowlist, header check) → classic classifier (logistic regression on a few hundred labelled examples) → small / System One model when inputs are too varied for features.
  • Requires a confidence threshold and an explicit fail-closed route for every gate: unsure escalates to the big model or a human, never guesses.
  • Optionally generates a baseline scaffold: label.py to sample ~200 real examples for hand-labelling, and baseline.py to train the classic classifier and print held-out accuracy — the number every vendor claim must beat.
  • Emits a ranked report sorted by savings, and explicitly leaves alone decisions that genuinely need the big model.

Who it's for

  • Teams running LLM agents or pipelines where API cost and latency hurt.
  • Backend / ML engineers cleaning up a "send everything to the big model" architecture.
  • Anyone adding a triage, routing, or filter layer in front of an agent.

Examples

  1. "Our support-ticket pipeline is too expensive — find what doesn't need the big model" → identifies spam filtering, language detection, and queue assignment, and maps them to rules plus a classifier.
  2. "I want a triage layer in front of my agent" → defines the label set, threshold, and escalation path for unsure cases.
  3. "A vendor claims 90% accuracy with their small model — is that good?" → builds a baseline on your own data with label.py and baseline.py for a fair comparison.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/undefined-ui/second-brain-os/HEAD/plugins/agents-course/skills/gate-check/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the plugins/agents-course/skills/gate-check folder from the GitHub repo undefined-ui/second-brain-os into my ~/.claude/skills/gate-check/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/undefined-ui/second-brain-os.git && mkdir -p ~/.claude/skills && cp -r second-brain-os/plugins/agents-course/skills/gate-check ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal.
  2. Clone the repo: git clone https://github.com/undefined-ui/second-brain-os.git
  3. Create the skills folder: mkdir -p ~/.claude/skills
  4. Copy just this skill: cp -r second-brain-os/plugins/agents-course/skills/gate-check ~/.claude/skills/
  5. Verify that ~/.claude/skills/gate-check/SKILL.md exists.
  6. Restart Claude Code, then ask something like "cut my model costs — run a gate check on this pipeline".
  7. Run it from the directory containing your pipeline code and prompts for the most accurate analysis.