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Agent Skill · 研究检索

tokenkiller(tokenkiller 文档)

tokenkiller 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

tokenkiller 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为操作浏览器;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 13,084。

总安装 9,709周安装 389GitHub Stars 1下载 3,143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
MIT-0
版本
1.0.1

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:tokenkiller(tokenkiller 文档)
来源仓库:https://github.com/coderangerx/tokenkiller
安装命令:
openclaw skills install tokenkiller
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install tokenkiller

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。

减少多技能代理工作流程中的令牌使用,采用预算与重复数据删除策略。适用于搜索、编码、调试等组合任务的效率提升。tokenkiller 属于研究检索类 Skill,可作为该场景下的辅助能力补充。通过 clawhub 安装,需确认是否启用渐进式披露与门控机制。建议核实各阶段输出截断规则与上下文压缩影响。

可查阅原始文档了解工作流集成与性能调优方法。

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 OpenClaw;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 openclaw skills install tokenkiller;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

    它需要操作浏览器,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

tokenkiller 怎么安装?

在终端执行 openclaw skills install tokenkiller。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

tokenkiller 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

tokenkiller 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

tokenkiller 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

tokenkiller 用的人多吗?

公开数据是累计安装 9,709 次,近一周安装 389 次,下载 3,143 次,GitHub 1 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。

tokenkiller 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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 OpenClaw,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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 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
tokenkiller

tokenkiller 文档 · 本页

9,7091操作浏览器MIT-0通过

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

name
tokenkiller
description
Reduces token usage across multi-skill agent workflows (search, coding, debugging, testing, docs) using budgets, gating, progressive disclosure, and deduped evidence. Use when the user mentions saving tokens, cost, context length, long logs, large codebases, or when tasks involve multi-step exploration or debugging.

TokenKiller (Universal Throttling)

Goal

Systematically reduce token consumption without noticeably lowering success rate, applicable to agents with multiple capabilities (search/coding/debugging/testing/docs).

Task Complexity Assessment

Before setting budgets, assess task complexity:

ComplexityCriteriaTool BudgetOutput Budget
SimpleSingle file modification, single-point localization, clear requirements≤3 calls≤50 lines
MediumAcross 2-3 files, needs simple exploration, relatively clear requirements≤6 calls≤120 lines
ComplexCross-module refactoring, multi-step debugging, unclear requirements≤10 calls≤200 lines

Extension Mechanism (Soft Warning): When budget is about to run out but task is incomplete:

  1. Output warning: [TokenKiller] Budget running low, current progress X/Y, remaining work: ...
  2. Continue execution, but switch to more conservative strategy
  3. User can interrupt or request more detailed output at any time

Default Working Mode (Balanced)

Global Hard Rules (Must Follow)

  • Goal First, Evidence Later: State the goal in one sentence (L0) first, then decide if evidence is needed (L2/L3).
  • Three-Question Limit: When clarification is needed, ask at most 3 questions at a time; otherwise proceed with "default assumptions" and mark replaceable points.
  • Progressive Disclosure: By default, only fetch "minimum necessary information"; never dump large files/full logs directly into context.
  • Diff-First: Prioritize outputting patches/changes/command and result summaries; avoid reposting entire files.
  • Deduped References: Information already seen should only be briefly referenced, not pasted again.

Budget Gate (Budget + Gate)

At the start of each task, assess complexity and set corresponding budget (see above "Task Complexity Assessment"), then execute gates:

  • Tool Call Budget: Set by complexity (Simple ≤3, Medium ≤6, Complex ≤10).
  • Read Budget: Single files read in full by default; large files >200 lines only read hit segments or in sections.
  • Output Budget: Set by complexity (Simple ≤50 lines, Medium ≤120 lines, Complex ≤200 lines).

If any gate is exceeded:

  • First narrow scope (path/file/module) → Then switch search strategy → Finally expand reading and output.

Token Consumption Self-Check

High-Consumption Behaviors (Avoid)

  • Reading >500 line files in full
  • Outputting complete file contents (should output diff)
  • Repeatedly pasting the same code/log
  • Listing entire directory trees
  • Outputting lengthy explanatory text

Self-Check Timing

After every 3 tool calls, quickly self-check:

  • Am I currently at L0-L2 level?
  • Is there duplicate information?
  • Is output exceeding necessary length?

Information Layers (L0-L3)

  • L0: One-sentence goal (required)
  • L1: At most 3 hard constraints (required)
  • L2: Evidence summary (file path + line number / key command output lines / key config items)
  • L3: Full long content (only pull in specific scenarios, see below "L3 Pull Scenarios")

Default output and context stay at L0-L2.

L3 Pull Scenarios (Explicit)

Only pull L3 (full content) in these scenarios:

  1. Code Modification: When exact indentation/format matching is needed, read target function's complete code
  2. Config Debugging: When config items are interdependent, need to see complete config block
  3. Error Analysis: When error message is incomplete, need complete stack trace or context
  4. User Explicit Request: User requests to see full content

Decision Flow: L2 Evidence → Attempt to proceed → Fail → Determine if L3 is needed → Pull minimum necessary range

Multi-Skill Collaboration

When this Skill is activated alongside other Skills:

Priority Rules

  • Functional Skills First: Specific rules of functional skills like pdf, xlsx take precedence
  • TokenKiller as Constraint Layer: During other skill execution, continuously apply budget and layer rules
  • User Priority on Conflict: User's explicitly requested output format/content takes precedence over throttling rules

Collaboration Mode

[User Request] → [Functional Skill Processing] → [TokenKiller Constrains Output]

Workflow (General)

1) Task Entry (Any Domain)

  1. Produce L0 + L1 (quickly infer if user didn't provide)
  2. Choose strategy (search/direct modification/verify first)
  3. Execute minimal action
  4. Immediately verify (cheapest verification first)
  5. Summarize: only key conclusion + 1 next step

2) Search/Exploration (Priority Domain)

Priority:

  1. Filename/Path (Glob)
  2. Exact String (Grep)
  3. Semantic Search (SemanticSearch)
  4. Read File (Read, by sections/line ranges)

Rules:

  • Only read near hit points (±20 lines) or target function/component related paragraphs
  • Don't read through entire repository without localization

3) Coding/Refactoring

Rules:

  • Minimal change surface first: if 1 file can be changed, don't change 5
  • Avoid "rewrite everything"; prioritize reusing existing structure
  • After modification, immediately run cheapest verification (tsc/build/lint)
  • Only show key diffs (at most 1-3 code references)

4) Debugging/Troubleshooting

Rules:

  • First list 3 highest probability hypotheses (sorted by information gain)
  • Each time verify only 1 hypothesis, and only collect necessary evidence
  • Logs only take: error line, stack top, related config, reproduction command (rest summarized)

5) Testing/Verification

Priority (from cheap to expensive):

  1. lint / typecheck
  2. build
  3. unit test
  4. e2e / browser automation

When failed, only append "diff information", don't repost full output.

6) Docs/Summary

Rules:

  • Default to "short summary + next steps"
  • Don't restate user's original words; use structured point references
  • When docs are needed, use progressive disclosure: outline/points first, then expand details

Output Template (Default)

Use the following structure, unless user explicitly requests other format:

  • Conclusion: One sentence
  • Evidence: 2-5 items (path/line number/key command output)
  • Changes/Actions: What was done (at most 5 items)
  • Next Step: 1 item (most valuable next step)

Trigger Words (Recommended Auto-Enable)

Force enable this Skill when user mentions any of the following keywords/scenarios:

  • "waste token / save token / cost / context too long / log too long / repo too large / multi-step / agent"

平台分布

OpenClaw

74.04%
按下载量换算2,327

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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。目前只有一个公开来源,建议同时核对源仓库。

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