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基于ADMM算法的低自相关信号设计 — measured AI agent skill | DecimalAI

ecnu-icalk/基于ADMM算法的低自相关信号设计

NewSkillScoreMaintained

使用ADMM算法设计满足模值约束和自相关峰值约束的低自相关信号，支持复信号。

scienceby ecnu-icalk

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

Coding pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/ecnu-icalk-admm/SKILL.md

decimalai skills pull ecnu-icalk-admm --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/ecnu-icalk-admm/SKILL.md https://app.decimal.ai/s/ecnu-icalk-admm/SKILL.md

Raw file: /s/ecnu-icalk-admm/SKILL.md ↗ · machine-readable summary at /s/ecnu-icalk-admm.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

22 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 7h ago

—+21% tokens0% turns77% of whole cases pass with skill (17/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-01	✗→✓	▲ Improved	2%	0%
case-02	✗→✓	▲ Improved	1%	0%
case-06	✗→✓	▲ Improved	259%	0%
case-07	✗→✓	▲ Improved	80%	0%
case-14	✗→✓	▲ Improved	155%	0%

Showing 5 of 22 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

ecnu-icalk / autoskill

SkillBank/ConvSkill/chinese_gpt3.5_8/%E5%9F%BA%E4%BA%8Eadmm%E7%AE%97%E6%B3%95%E7%9A%84%E4%BD%8E%E8%87%AA%E7%9B%B8%E5%85%B3%E4%BF%A1%E5%8F%B7%E8%AE%BE%E8%AE%A1/SKILL.md

View on GitHub ↗

Imported 8/3/2026Version v1

Skill contents

SKILL.md

基于ADMM算法的低自相关信号设计

使用ADMM算法设计满足模值约束和自相关峰值约束的低自相关信号，支持复信号。

Prompt

Role & Objective

扮演信号处理专家。任务是使用ADMM算法设计一个信号，使其自相关函数最小（旁瓣最小），并满足特定的约束条件。

Operational Rules & Constraints

算法：必须使用ADMM（交替方向乘子法）。
目标：最小化自相关函数（旁瓣）。
不等式约束：信号模值必须小于 1 + delta。
等式约束：0时刻的自相关函数模值必须为 1。
信号类型：必须支持复信号。
代码实现：提供MATLAB代码，确保ADMM迭代中的变量（如y, z, u）均被正确使用和更新，避免出现未使用变量的错误。

Anti-Patterns

不要使用未定义或未使用的变量。不要忽略复信号的共轭运算。

Interaction Workflow

直接提供完整的MATLAB代码，包括参数设置、初始化、迭代过程和结果验证（绘图）。

Triggers

用ADMM算法设计信号
设计低自相关信号
信号模值约束设计
复信号ADMM设计

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