Borrowing it
Nothing to install: this file belongs to u9401066/nsforge-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/u9401066/nsforge-mcp/master/.claude/skills/nsforge-verification-suite/SKILL.mdgit clone --depth 1 https://github.com/u9401066/nsforge-mcpWrote 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/u9401066/nsforge-mcp/nsforge-verification-suite)<a href="https://agentmods.dev/skills/u9401066/nsforge-mcp/nsforge-verification-suite"><img src="https://agentmods.dev/badge/skills/u9401066/nsforge-mcp/nsforge-verification-suite/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/u9401066/nsforge-mcp/nsforge-verification-suite"><img src="https://agentmods.dev/badge/skills/u9401066/nsforge-mcp/nsforge-verification-suite.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00030 | $0.00655 |
| Opus 5 | $0.00015 | $0.00328 |
| Sonnet 5 | $0.00006 | $0.00131 |
| Haiku 4.5 | $0.00003 | $0.00065 |
Grade A, and why
nsforge-verification-suite 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 9d 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.
What it actually says
驗證工具 Skill
⚠️ 驗證後必須向用戶展示結果！
- 驗證成功/失敗都要清楚告知用戶
- 維度分析結果要用人類可讀格式展示
工具速查
| 驗證類型 | 工具 | 參數 |
|---|---|---|
| 符號等價 | symbolic_equal(expr1, expr2) |
兩表達式 |
| 導數驗證 | verify_derivative(original, claimed, var) |
原式、宣稱導數、變數 |
| 積分驗證 | verify_integral(original, claimed, var) |
被積函數、宣稱積分、變數 |
| 解驗證 | verify_solution(equation, solution, var) |
方程、解、變數 |
| 維度分析 | check_dimensions(expr, units_map) |
表達式、單位映射 |
調用範例
# 符號等價
symbolic_equal("(x+1)**2", "x**2 + 2*x + 1") # → True
# 導數驗證：d/dx[ln(x²)] = 2/x ?
verify_derivative("ln(x**2)", "2/x", "x") # → correct: True
# 積分驗證：∫sin(x)dx = -cos(x) ?
verify_integral("sin(x)", "-cos(x)", "x") # → correct: True
# 解驗證：x=2 是 x²-4=0 的解？
verify_solution("x**2 - 4", "2", "x") # → correct: True
# 維度分析
check_dimensions("m * a", {"m": "kg", "a": "m/s**2"})
# → dimension: [mass]*[length]/[time]**2 = Force
常用單位映射
# 力學
{"F": "N", "m": "kg", "a": "m/s**2", "v": "m/s", "t": "s"}
# 藥動學
{"C": "mg/L", "V": "L", "k": "1/h", "t": "h", "D": "mg"}
# 熱力學
{"T": "K", "E": "J", "R": "J/(mol*K)", "n": "mol"}
驗證順序建議
- 維度分析 - 維度錯，結果必錯
- 符號驗證 - 代數正確性
- 數值抽樣 - 特殊情況
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.
- 9d ago First seen · 60 lines · 30 tokens per session scan A adb7534004e2
nsforge-verification-suite is a skill published in the GitHub repository u9401066/nsforge-mcp (4 stars, last pushed 9d ago), licensed Apache-2.0. It adds 30 tokens to every session and 655 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
drug-discovery
Drug discovery: ChEMBL search, drug-likeness, interactions.
jupyter-notebook
Iterative Python via live Jupyter kernel (hamelnb).
batch-processing-clinical-text
Run large-scale batch NER, PII extraction, or de-identification over many clinical notes on-device with OpenMed, with sharding, checkpointing, resumability, and append-only JSONL output. Use when the user needs to process a corpus or folder of notes, de-identify a dataset, run NER over thousands of documents, build a…
coding-hcc-risk-adjustment
Maps chronic conditions extracted by OpenMed to CMS-HCC V28 risk-adjustment categories and estimates a RAF (Risk Adjustment Factor) score as decision support. Use when the user wants to surface risk-adjustable diagnoses from notes, map ICD-10-CM codes to HCC categories, estimate or reconcile a patient/panel RAF, find…
detecting-pv-signals
Computes disproportionality signals — PRR, ROR, EBGM, and IC (BCPNN) — over FAERS / OpenFDA drug-event data to flag potential safety signals. Use when the user wants to mine spontaneous-report data for drug-reaction associations, build a 2x2 contingency table, compute a Proportional Reporting Ratio or Reporting Odds…
structuring-radiology-reports
Converts free-text radiology narratives into structured findings and impression — with measurements, laterality, anatomy, and follow-up recommendations — after OpenMed NER. Use when the user has a CT/MRI/X-ray/ultrasound/mammography report and needs the sections split (technique, comparison, findings, impression)…