V0.8.0
Agent Skill for constrained optimization via ALM/ADMM/KKT — with adversarial guards, COOP cross-skill protocol, and 20k-sim validated parameters
npx -y skills add Sliky1/lagrangian-skills --skill v0.8.0Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Augmented Lagrangian for constrained optimization. Handles convex QP, smooth NLP, non-convex NLP, distributed ADMM, Safe RL, multi-objective. Trigger on: constrained optimization, KKT, ALM, ADMM, safe constraints, Pareto front, shadow prices, sensitivity analysis, infeasibility diagnosis, near-infeasible problems.
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
3.4 KB, as published. Nobody here has run it
Lagrangian Core Skill — v0.8.0
能力边界
支持: 凸QP | 光滑NLP | 非凸NLP | 分布式ADMM | Safe RL | 多目标 协同: 检测贝叶斯/统计成分→HALT并建议调用对应Skill 不支持: 纯贝叶斯 | 纯统计检验 | MIP → HALT 输出模式: MINIMAL(~0.10x,"只要数字") | STANDARD(~1.13x,默认) | VERBOSE(~1.55x,"展开计算") 业务语言翻译层默认关闭,"解释含义"时开启。
Step -1 — 预检 [LAT-1] (4项并行, ~60ms)
- 变量类型 2. 约束可行性(LP松弛) 3. 问题规模 4. 量纲一致性 任意HALT条件 → 立即停止,输出结构化错误码。
Step 0 — 澄清
模糊点→单轮确认;贝叶斯信号→HALT "请调用贝叶斯Skill"
| 边界类型 | 触发词 | 处理方式 |
|---|---|---|
| 定性目标 | 公平/均衡/合理/尽量 | Max-Min/基尼/等比例选项 |
| 模糊数值 | 大约/左右/差不多 | 严格上限/软约束/范围选项 |
| OR约束 | 或/至少一个/二选一 | MIP/smooth_max/拆分选项 |
| 单位歧义 | 混合量纲 | 展示解析表请用户确认 |
| 条件逻辑 | 如果则/当时/第X期 | 合并/MIP/惩罚项选项 |
Step 3 — 稀疏JSON通道 [TOK-7/11]
只输出非默认字段:
{"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 [TOK-10/15]
指纹=(变量数, eq约束数, ineq约束数, 目标函数类型, 约束结构哈希); 命中率~85%
Step 5 — 求解路由 [FIX-16/17/18]
safe_rl+adversarial → cos_thresh=0.10, window=20
safe_rl+near_infeas → ratio_thresh=3.0, n_stages=6, stage_step=0.25
multi_obj+adversarial → max_repair=3, repair_freq=10
non_convex+adversarial → ALM(n_starts=10, uniform_random)
non_convex+normal → ALM(n_starts=10, warm_start=cache)
convex_qp/smooth_nlp → standard_solver
distributed → ADMM
非凸问题Step 1只输出结论。[TOK-17]
Step 6 — 影子价格 [TOK-12]
默认只输出活跃约束(影子价格>0);其余折叠"[展开]"。
Step 7 — 自然语言渲染 [TOK-7]
STANDARD: 最优解(一行) → 约束状态表(仅活跃) → 关键瓶颈(一句) VERBOSE: STANDARD + Steps 3-6 JSON原始数据
失败处理 [UX-5/6, TOK-14]
{"status":"FAILED","error_code":"INFEASIBLE|BAD_PARAMS|AMBIGUOUS|SOLVER_FAIL",
"reason":"<一行说明>","recovery":"<修复建议或最小松弛量>"}
近不可行→自动计算最小松弛量写入recovery。
Forbidden Behaviors
❌ Steps 1-6输出自然语言 | ❌ Step 7输出JSON给用户 ❌ 语言边界直接HALT | ❌ 失败后输出散文 ❌ JSON含默认值 | ❌ 不活跃约束默认展开 ❌ FIX-16: cos_thresh>0.20或window<15 ❌ FIX-17: repair_freq<5 ❌ FIX-18: stage_step>0.40或n_stages∉[5,7]