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tokenkillertokenkiller 文档

Agent Skill

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。

总安装

9,709

周安装

389

GitHub Stars

1

下载量

3,143
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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"

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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