calibrate-dataset

calibrate-dataset is a skill for Claude Code from wang90063/nslb-optimization-agent. It costs 119 tokens per session (1,076 once invoked), scanned A, original, no licence file.

A dataset-calibration workflow for NSLB candidates: it reviews existing test cases using results from repeated online submissions and places them into groups based on whether they predict those results.

In plain words
What is it for?
It is for reclassifying accumulated candidate cases into submitcore or contrast after several online submissions. NSLB is not explained by the input.
Why use it?
It helps correct which existing cases are useful or misleading according to real submission feedback, without creating new cases.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

Good fit It is for reclassifying accumulated candidate cases into submitcore or contrast after several online submissions. NSLB is not explained by the input.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/wang90063/nslb-optimization-agent/calibrate-dataset
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 wang90063/nslb-optimization-agent --skill calibrate-dataset
Clone the repo
git clone --depth 1 https://github.com/wang90063/nslb-optimization-agent

Made for: Claude Code.

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 calibrate-dataset

README.md
[![agentmods](https://agentmods.dev/badge/skills/wang90063/nslb-optimization-agent/calibrate-dataset.svg)](https://agentmods.dev/skills/wang90063/nslb-optimization-agent/calibrate-dataset)
Your own site
<a href="https://agentmods.dev/skills/wang90063/nslb-optimization-agent/calibrate-dataset"><img src="https://agentmods.dev/badge/skills/wang90063/nslb-optimization-agent/calibrate-dataset.svg" alt="Measured on agentmods" height="20"></a>
Per session 119 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,076 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.
Origin unknown 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 $0.00119 $0.01076
Opus 5 $0.00060 $0.00538
Sonnet 5 $0.00024 $0.00215
Haiku 4.5 $0.00012 $0.00108

Measured 7d ago against content hash 6c45ff53dc2b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

calibrate-dataset 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 7d 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.

.claude/skills/calibrate-dataset/SKILL.md · 48 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Files

What ships with it

2 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.

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. 7d ago First seen · 48 lines · 119 tokens per session scan A 6c45ff53dc2b

Subscribe to this mod's changes

calibrate-dataset is a skill published in the GitHub repository wang90063/nslb-optimization-agent (11 stars, last pushed 2mo ago), with no licence file. It adds 119 tokens to every session and 1,076 once invoked, about $0.0006 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 2026-08-30.

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