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-derivation-workflow/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-derivation-workflow)<a href="https://agentmods.dev/skills/u9401066/nsforge-mcp/nsforge-derivation-workflow"><img src="https://agentmods.dev/badge/skills/u9401066/nsforge-mcp/nsforge-derivation-workflow/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-derivation-workflow"><img src="https://agentmods.dev/badge/skills/u9401066/nsforge-mcp/nsforge-derivation-workflow.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 | $0.00035 | $0.01398 |
| Opus 5 | $0.00017 | $0.00699 |
| Sonnet 5 | $0.00007 | $0.00280 |
| Haiku 4.5 | $0.00003 | $0.00140 |
Grade A, and why
nsforge-derivation-workflow 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
推導工作流 Skill
核心原則
SymPy-MCP 做計算，NSForge 記錄知識！ 每步都要顯示給用戶看！ 使用：
derivation_show()(NSForge) - 顯示當前推導狀態print_latex_expression()(SymPy-MCP) - 顯示計算結果
⚠️ 黃金法則：永遠向用戶展示公式！
❌ 錯誤：執行計算後直接下一步
✅ 正確：執行計算 → derivation_show() 或 print_latex_expression() → 等用戶確認 → 下一步
工作流程
Phase 1: derivation_start(name, description)
↓
Phase 2: 循環 {
SymPy-MCP: intro_many → introduce_expression → 計算 → print_latex_expression
NSForge: derivation_record_step(expression, description, notes?)
NSForge: derivation_show() ← 🆕 顯示當前狀態！
NSForge: derivation_add_note(note, note_type?) # 可選
}
↓
Phase 3: derivation_complete(description, assumptions?, limitations?, references?)
derivation_show() ← 🆕 顯示最終結果！
工具速查
| 階段 | MCP | 工具 | 用途 |
|---|---|---|---|
| 開始 | NSForge | derivation_start(name, description) |
建立會話 |
| 計算 | SymPy | intro_many, introduce_expression, substitute_expression... |
符號計算 |
| 顯示 | SymPy | print_latex_expression |
⚠️ 必須！ |
| 記錄 | NSForge | derivation_record_step(expr, desc, notes?, source?) |
記錄步驟+知識 |
| 🆕 顯示 | NSForge | derivation_show(format?, show_steps?) |
⚠️ 必須！顯示當前狀態 |
| 說明 | NSForge | derivation_add_note(note, note_type?) |
純文字洞見 |
| 完成 | NSForge | derivation_complete(...) |
存檔+元資料 |
note_type: assumption, limitation, observation, correction, clinical, physical
🆕 步驟 CRUD 操作
| 操作 | 工具 | 用途 |
|---|---|---|
| 📖 Read | derivation_get_step(step_number) |
查看單一步驟詳情 |
| ✏️ Update | derivation_update_step(step_number, notes?, assumptions?, ...) |
更新步驟元資料 |
| 🗑️ Delete | derivation_delete_step(step_number) |
刪除最後一步 |
| ⏪ Rollback | derivation_rollback(to_step) |
⚡ 回滾到指定步驟 |
| 📝 Insert | derivation_insert_note(after_step, note, note_type?) |
在指定位置插入說明 |
Handoff：NSForge 做不到時
⚠️ Phase 2 後更新：Laplace/Fourier 變換已實作，無需 Handoff！
當需要 ODE/PDE、矩陣運算、聯立方程組：
# 1. 導出
result = derivation_export_for_sympy()
# → 返回 intro_many_command, current_expression
# 2. SymPy-MCP 計算
intro_many([...])
dsolve_ode(...) / solve_linear_system(...)
print_latex_expression(...)
# 3. 導入回 NSForge
derivation_import_from_sympy(
expression="...",
operation_performed="Solved ODE",
sympy_tool_used="dsolve_ode",
notes="...",
assumptions_used=[...],
limitations=[...]
)
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.
- 8d ago First seen · 145 lines · 35 tokens per session scan A 16d5dd380ba1
nsforge-derivation-workflow is a skill published in the GitHub repository u9401066/nsforge-mcp (4 stars, last pushed 8d ago), licensed Apache-2.0. It adds 35 tokens to every session and 1,398 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.
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