Token导航 LogoToken导航

Agent Skill · 研究检索

ln-811-algorithm-optimizer(ln 811 算法优化器)

ln-811-algorithm-optimizer 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

ln-811-algorithm-optimizer 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 45,481。

总安装 847周安装 36GitHub Stars 441下载 297核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ln-811-algorithm-optimizer(ln 811 算法优化器)
来源仓库:https://github.com/levnikolaevich/claude-code-skills
仓库路径:skills/ln-811-algorithm-optimizer
安装命令:
npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-811-algorithm-optimizer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-811-algorithm-optimizer

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息。ln-811-algorithm-optimizer 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适合根据关键词或任务场景快速定位候选结果。可结合来源仓库和原始 README 核验具体用法。安装前建议确认权限范围和维护状态。注意是否会触发联网、命令执行或文件读写操作。

来源仓库内路径:skills/ln-811-algorithm-optimizer

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-8…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

ln-811-algorithm-optimizer 怎么安装?

在终端执行 npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-811-algorit…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ln-811-algorithm-optimizer 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

ln-811-algorithm-optimizer 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

ln-811-algorithm-optimizer 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

ln-811-algorithm-optimizer 用的人多吗?

公开数据是累计安装 847 次,近一周安装 36 次,下载 297 次,GitHub 441 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

ln-811-algorithm-optimizer 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
ln-811-algorithm-optimizer

ln 811 算法优化器 · 本页

847441调用外部服务未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Paths: File paths (shared/, references/, ../ln-*) are relative to skills repo root. If not found at CWD, locate this SKILL.md directory and go up one level for repo root.

ln-811-algorithm-optimizer

Type: L3 Worker Category: 8XX Optimization Parent: ln-810-performance-optimization-coordinator

Optimizes target function performance via autoresearch loop: establish baseline benchmark, research best practices, generate 3-7 hypotheses, apply each with keep/discard verification.


Overview

AspectDetails
Inputtarget_file + target_function (or audit findings from ln-650)
OutputOptimized function with benchmark proof, optimization report
PatternAutoresearch: modify → benchmark → keep (≥10%) / discard

Workflow

Phases: Pre-flight → Baseline → Research → Hypothesize → Optimize Loop → Report


Phase 0: Pre-flight Checks

CheckRequiredAction if Missing
Target file existsYesBlock optimization
Target function identifiableYesBlock optimization
Test infrastructureYesBlock optimization (see ci_tool_detection.md)
Test coverage for target functionYesBlock — no coverage = no safety net
Git clean stateYesBlock (need clean baseline for revert)
Benchmark infrastructureNoGenerate benchmark (see references)

MANDATORY READ: Load shared/references/ci_tool_detection.md — use Benchmarks + Test Frameworks sections.

Coverage Verification

Before starting optimization, verify target function has test coverage:

StepAction
1Grep test files for target function name / imports from target module
2If ≥1 test references target → PROCEED
3If 0 tests reference target → BLOCK with "no test coverage for {function}"
Without test coverage, benchmark improvements are meaningless — the optimized function may produce wrong results faster.

Worktree & Branch Isolation

MANDATORY READ: Load shared/references/git_worktree_fallback.md — use ln-811 row.

All work (edits, benchmarks, KEEP commits) in worktree. Never modify main worktree.


Phase 1: Establish Baseline

Step 1.1: Detect or Generate Benchmark

SituationAction
Existing benchmark foundUse as-is
No benchmark existsGenerate minimal benchmark (see benchmark_generation.md)

Step 1.2: Run Baseline

ParameterValue
Runs5
MetricMedian execution time
Warm-up1 discarded run
Outputbaseline_median, baseline_p95

Save baseline result — all improvements measured against this.


Phase 2: Research Best Practices

MANDATORY READ: Load shared/references/research_tool_fallback.md for MCP tool chain.

Research Strategy

PriorityToolQuery Template
1mcp__context7__query-docs"{language} {algorithm_type} optimization techniques"
2mcp__Ref__ref_search_documentation"{language} {function_name} performance best practices"
3WebSearch"{algorithm_type} optimization {language} benchmark {current_year}"

Research Output

Collect optimization techniques applicable to the target function. For each technique note:

  • Name and description
  • Expected improvement category (algorithmic complexity, memory, cache, parallelism)
  • Applicability conditions (data size, structure, language features)

Phase 3: Generate Hypotheses (3-7)

Hypothesis Sources

MANDATORY READ: Load optimization_categories.md for category checklist.

SourcePriority
Research findings (Phase 2)1
Optimization categories checklist2
Code analysis (anti-patterns in target)3

Hypothesis Format

FieldDescription
idH1, H2,... H7
categoryFrom optimization_categories.md
descriptionWhat to change
expected_impactEstimated improvement %
riskLow / Medium / High
dependenciesOther hypotheses this depends on

Ordering

Sort by: expected_impact DESC, risk ASC. Independent hypotheses first (no dependencies).


Phase 4: Optimize Loop (Keep/Discard)

Per-Hypothesis Cycle

FOR each hypothesis (H1..H7):
  1. APPLY: Edit target function (surgical change, function body only)
  2. VERIFY: Run tests
     IF tests FAIL (assertion) → DISCARD (revert) → next hypothesis
     IF tests CRASH (runtime error, OOM, import error):
       IF fixable (typo, missing import) → fix & re-run ONCE
       IF fundamental (design flaw, incompatible API) → DISCARD + log "crash: {reason}"
  3. BENCHMARK: Run 5 times, take median
  4. COMPARE: improvement = (baseline - new) / baseline * 100
     IF improvement >= 10% → KEEP:
       git add {target_file}
       git commit -m "perf(H{N}): {description} (+{improvement}%)"
       new baseline = new median
     IF improvement < 10%  → DISCARD (revert edit)
  5. LOG: Record result to experiment log + report

Safety Rules

RuleDescription
ScopeOnly target function body; no signature changes
DependenciesNo new package installations
Revertgit checkout -- {target_file} on discard
Time budget30 minutes total for all hypotheses
CompoundEach KEEP becomes new baseline for next hypothesis
TraceabilityEach KEEP = separate git commit with hypothesis ID in message
IsolationAll work in isolated worktree; never modify main worktree

Keep/Discard Decision

ConditionDecisionAction
Tests failDISCARDRevert, log reason
Improvement ≥ 10%KEEPUpdate baseline
Improvement 10-20% BUT complexity increaseREVIEWLog as "marginal + complex", prefer DISCARD
Improvement < 10%DISCARDRevert, log as "insufficient gain"
Regression (slower)DISCARDRevert, log regression amount
Simplicity criterion (per autoresearch): If improvement is marginal (10-20%) and change significantly increases code complexity (>50% more lines, deeply nested logic, hard-to-read constructs), prefer DISCARD. Simpler code at near-equal performance wins.

Phase 5: Report Results

Report Schema

FieldDescription
targetFile path + function name
baselineOriginal median benchmark
finalFinal median after all kept optimizations
total_improvementPercentage improvement
hypotheses_testedTotal count
hypotheses_keptCount of kept optimizations
hypotheses_discardedCount + reasons
optimizations[]Per-kept: id, category, description, improvement%

Experiment Log

Write to {project_root}/.optimization/ln-811-log.tsv:

ColumnDescription
timestampISO 8601
hypothesis_idH1..H7
categoryFrom optimization_categories.md
descriptionWhat changed
baseline_msBaseline median before this hypothesis
result_msNew median after change
improvement_pctPercentage change
statuskeep / discard / crash
commitGit commit hash (if kept)

Append to existing file if present (enables tracking across multiple runs).

Cleanup

ActionWhen
Remove generated benchmarkIf benchmark was auto-generated AND no kept optimizations
Keep generated benchmarkIf any optimization was kept (proof of improvement)

Configuration

Options:
  # Target
  target_file: ""
  target_function: ""

  # Benchmark
  benchmark_runs: 5
  improvement_threshold: 10    # percent
  warmup_runs: 1

  # Hypotheses
  max_hypotheses: 7
  min_hypotheses: 3

  # Safety
  time_budget_minutes: 30
  allow_new_deps: false
  scope: "function_body"       # function_body | module

  # Verification
  run_tests: true
  run_lint: false

Error Handling

ErrorCauseSolution
No benchmark frameworkStack not in ci_tool_detection.mdGenerate inline benchmark
All hypotheses discardedNo effective optimization foundReport "no improvements found"
Benchmark noise too highInconsistent timingIncrease runs to 10, use p50
Test flakeNon-deterministic testRe-run once; if flakes again, skip hypothesis

References


Definition of Done

  • Test coverage for target function verified before optimization
  • Target function identified and baseline benchmark established (5 runs, median)
  • Research completed via MCP tool chain (Context7/Ref/WebSearch)
  • 3-7 hypotheses generated, ordered by expected impact
  • Each hypothesis tested: apply → tests → benchmark → keep/discard
  • Each kept optimization = separate git commit with hypothesis ID
  • Kept optimizations compound (each becomes new baseline)
  • Marginal gains (10-20%) with complexity increase reviewed via simplicity criterion
  • Tests pass after all kept optimizations
  • Experiment log written to .optimization/ln-811-log.tsv
  • Report returned with baseline, final, improvement%, per-hypothesis results
  • Generated benchmark cleaned up if no optimizations kept
  • All changes on isolated branch, pushed to remote

Version: 1.0.0 Last Updated: 2026-03-08

平台分布

Codex

34.59%
按下载量换算103

Claude

32.41%
按下载量换算96

Cursor

19.04%
按下载量换算57

Gemini CLI

10.54%
按下载量换算31

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

可疑

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills