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.
npx skills add Sliky1/lagrangian-skills --skill v0.3.0git clone --depth 1 https://github.com/Sliky1/lagrangian-skillsWrote 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/sliky1/lagrangian-skills/v0.3.0)<a href="https://agentmods.dev/skills/sliky1/lagrangian-skills/v0.3.0"><img src="https://agentmods.dev/badge/skills/sliky1/lagrangian-skills/v0.3.0/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/sliky1/lagrangian-skills/v0.3.0"><img src="https://agentmods.dev/badge/skills/sliky1/lagrangian-skills/v0.3.0.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 | $0.00059 | $0.00379 |
| Opus 5 | $0.00030 | $0.00189 |
| Sonnet 5 | $0.00012 | $0.00076 |
| Haiku 4.5 | $0.00006 | $0.00038 |
Grade A, and why
lagrangian-core 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 11d 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
Lagrangian Core Skill — v0.3.0
能力边界
支持: 凸QP | 光滑NLP | 非凸NLP(multi-start) | 分布式ADMM 不支持: Safe RL | 多目标 | 贝叶斯混合
方法路由
convex_qp/smooth_nlp → standard_solver
non_convex → ALM(n_starts=5, uniform_random)
distributed → ADMM
Step 3 — 稀疏JSON通道
{"step":3,"type":"augmented_lagrangian",
"formula":"L_ρ=f(x)+Σλ·h(x)+Σμ·g(x)+ρ/2·||h||²",
"multipliers":{"lambda":[0.0],"mu":[0.0]},
"penalty":{"rho_init":1.0,"update_rule":"×1.5 if ||h||>tol"}}
Step 4 — KKT验证
缓存指纹=(变量数, eq约束数, ineq约束数, 目标函数类型)
Step 7 — 输出
最优解(一行) → 约束状态 → KKT残差
Forbidden Behaviors
❌ Steps 1-6输出自然语言 ❌ 失败后输出散文
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.
- 11d ago First seen · 43 lines · 59 tokens per session scan A 9bdfd91ae8e6
lagrangian-core is a skill published in the GitHub repository Sliky1/lagrangian-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 59 tokens to every session and 379 once invoked, about $0.0003 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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