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Agent Skill · 研究检索

input-classification-v1(输入分类 v1)

input-classification-v1 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。

input-classification-v1 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;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 8,929。

总安装 15,278周安装 643GitHub Stars 公开资料未说明下载 5,350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
1.0.0

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:input-classification-v1(输入分类 v1)
来源仓库:https://github.com/omprasad122007-rgb/input-classification-v1
安装命令:
openclaw skills install input-classification-v1
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 input-classification-v1

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。

input-classification-v1 基于规则系统对输入进行任务分类与复杂度评估。适用于明确意图识别与执行资源预估等预处理环节。通过 clawhub 安装后,可自定义分类规则与权重参数。使用前应确保规则集覆盖主要用户场景避免漏判。建议定期审核规则有效性并根据反馈动态调整。

input-classification-v1 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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 input-classification-v1;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

input-classification-v1 怎么安装?

在终端执行 openclaw skills install input-classification-v1。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

input-classification-v1 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。

input-classification-v1 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

input-classification-v1 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。

input-classification-v1 用的人多吗?

公开数据是累计安装 15,278 次,近一周安装 643 次,下载 5,350 次。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。

input-classification-v1 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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
input-classification-v1

输入分类 v1 · 本页

15,278—只读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
input-classification-system
description
Deterministic rule-based system for classifying clarified input into a single primary task category and assigning execution complexity. Use when the Main Agent needs to categorize user requests before task decomposition, route tasks to appropriate handlers, assess complexity and risk levels, or determine if clarification is needed. Triggers after clarification is complete and before decomposition begins.

Input Classification System

1. Skill Name

Input Classification System

Identifier: input-classification-system

2. Version

1.0

This is the initial release of the Input Classification System skill.

3. Skill Purpose

Provide a deterministic, rule-based classification system that enables the Main Agent to categorize clarified user input into exactly one primary task category, assign execution complexity, assess risk level, and determine confidence score before any task decomposition or execution planning occurs.

Measurable Objectives:

  • Achieve 100% single-category classification (no ambiguous multi-category outputs)
  • Provide deterministic tie-breaking for all edge cases
  • Assign complexity levels using measurable thresholds
  • Calculate confidence scores with explicit formulas
  • Route ambiguous inputs to clarification with specific triggers

4. What This Skill Does

  • Classifies clarified input into exactly one primary category from a fixed list
  • Assigns complexity level based on measurable operation counts and time estimates
  • Calculates risk level based on impact scope and reversibility criteria
  • Computes confidence score using explicit scoring factors
  • Determines if input requires additional clarification
  • Sets the appropriate state transition after classification
  • Applies deterministic tie-breaking logic when multiple categories match
  • Validates that input has been clarified before classification
  • Logs all classification decisions with full context
  • Provides secondary tags for additional context (maximum 3)
  • Enforces boundary definitions between categories
  • Triggers escalation for high-risk or low-confidence classifications
  • Prevents misclassification through explicit rules
  • Outputs structured ClassificationResult for downstream systems
  • Maintains audit trail for all classification decisions

5. What This Skill Must Not Do

  • Must not perform task decomposition or breakdown
  • Must not solve or execute any tasks
  • Must not create execution plans or strategies
  • Must not apply emotional reasoning or sentiment analysis
  • Must not assign multiple primary categories to a single input
  • Must not overlap with Clarification System responsibilities
  • Must not modify the original input content
  • Must not make assumptions about user intent without explicit indicators
  • Must not skip complexity assessment for any classification
  • Must not bypass risk assessment for any classification
  • Must not assign confidence scores below threshold without escalation
  • Must not classify inputs that have not been clarified first
  • Must not use probabilistic or non-deterministic classification methods
  • Must not ignore tie-breaking rules when categories conflict
  • Must not proceed to execution without setting state transition

6. Activation Conditions

This skill activates when ALL of the following conditions are met:

  1. Clarification Complete: Input has been processed by the Clarification System and marked as clarified
  2. No Pending Questions: No outstanding clarification questions remain
  3. Classification Not Yet Performed: Input has not been previously classified
  4. Valid Input Structure: Input contains recognizable task indicators

Pre-Activation Checklist:

  • [ ] Input marked as "clarified" by Clarification System
  • [ ] No clarification questions pending
  • [ ] Input contains actionable request
  • [ ] Input is not empty or malformed

Do NOT activate if:

  • Input is still being clarified
  • Input is a clarification question itself
  • Input is a response to a clarification question
  • Input has already been classified

7. Classification Categories

The following 15 categories form the fixed classification list:

CategoryCodeDescription
CODE_GENERATIONCGWriting, modifying, or generating source code
CODE_REVIEWCRReviewing, analyzing, or auditing existing code
DEBUGGINGDBIdentifying and fixing bugs, errors, or issues
DATA_ANALYSISDAAnalyzing, processing, or visualizing data
FILE_OPERATIONSFOReading, writing, moving, or managing files
DOCUMENTATIONDCCreating or updating documentation
REFACTORINGRFRestructuring code without changing behavior
TESTINGTSWriting, running, or managing tests
DEPLOYMENTDPDeploying applications or infrastructure
RESEARCHRSInvestigating, searching, or gathering information
CONFIGURATIONCFSetting up or modifying configurations
COMMUNICATIONCMDrafting messages, emails, or communications
CONVERSIONCVTransforming data or files between formats
ANALYSISANGeneral analysis not covered by other categories
PLANNINGPLCreating plans, strategies, or roadmaps

Category Priority Order (for tie-breaking):

  1. DEBUGGING (highest - immediate attention needed)
  2. DEPLOYMENT
  3. CODE_GENERATION
  4. REFACTORING
  5. TESTING
  6. CODE_REVIEW
  7. DATA_ANALYSIS
  8. CONFIGURATION
  9. FILE_OPERATIONS
  10. CONVERSION
  11. DOCUMENTATION
  12. RESEARCH
  13. ANALYSIS
  14. COMMUNICATION
  15. PLANNING (lowest)

8. Category Boundary Definitions

CODE_GENERATION vs CODE_REVIEW

  • CODE_GENERATION: Input requests creating new code or modifying existing code
  • CODE_REVIEW: Input requests analysis of existing code without modifications
  • Boundary: If modification is implied, classify as CODE_GENERATION

CODE_GENERATION vs REFACTORING

  • CODE_GENERATION: Creating new functionality or features
  • REFACTORING: Restructuring existing code without new functionality
  • Boundary: "Improve performance" without feature change = REFACTORING

DEBUGGING vs CODE_GENERATION

  • DEBUGGING: Input explicitly mentions errors, bugs, or failures
  • CODE_GENERATION: Input requests new features without error context
  • Boundary: "Fix this bug" = DEBUGGING; "Add error handling" = CODE_GENERATION

DATA_ANALYSIS vs ANALYSIS

  • DATA_ANALYSIS: Input involves data processing, statistics, or visualization
  • ANALYSIS: Input involves general analysis of concepts, requirements, or situations
  • Boundary: If data files/sets are mentioned = DATA_ANALYSIS

FILE_OPERATIONS vs CONVERSION

  • FILE_OPERATIONS: Input requests file management (read, write, move, delete)
  • CONVERSION: Input requests format transformation between file types
  • Boundary: "Convert X to Y format" = CONVERSION; "Read file X" = FILE_OPERATIONS

RESEARCH vs ANALYSIS

  • RESEARCH: Input requests information gathering or investigation
  • ANALYSIS: Input requests evaluation or assessment of known information
  • Boundary: "Find information about X" = RESEARCH; "Evaluate X" = ANALYSIS

TESTING vs CODE_GENERATION

  • TESTING: Input specifically requests test creation or test execution
  • CODE_GENERATION: Input requests production code
  • Boundary: "Write tests for X" = TESTING; "Write X with tests" = CODE_GENERATION (primary)

CONFIGURATION vs DEPLOYMENT

  • CONFIGURATION: Input requests setup of settings or configurations
  • DEPLOYMENT: Input requests deployment to environments or infrastructure
  • Boundary: Local setup = CONFIGURATION; Remote/environment setup = DEPLOYMENT

9. Single-Primary-Category Rule

Rule: Every classified input MUST have exactly one primary category.

Enforcement Rules

  1. No Multi-Category Output: Never output multiple primary categories
  2. Tie-Breaking Required: When multiple categories match, apply tie-breaking logic
  3. Category Exclusivity: Primary category is mutually exclusive with other primary categories

Tie-Breaking Logic

When input matches multiple categories, apply in order:

Step 1: Keyword Dominance

  • Count explicit keyword matches for each candidate category
  • Category with highest keyword count wins
  • If tied, proceed to Step 2

Step 2: Action Verb Analysis

  • Identify primary action verb in input
  • Map action verb to category using verb-to-category mapping
  • If still tied, proceed to Step 3

Step 3: Priority Order

  • Apply category priority order (see Section 7)
  • Higher priority category wins

Step 4: Default Fallback

  • If all steps fail, default to ANALYSIS category

Tie-Breaking Example

Input: "Debug and fix the error in the authentication code, then add logging"

  1. Keywords: DEBUGGING (debug, fix, error), CODE_GENERATION (add, logging)
  2. Action Verb: "Debug" (primary action) → DEBUGGING
  3. Result: DEBUGGING (primary), secondary_tags: [CODE_GENERATION]

10. Secondary Tag Rules

Secondary tags provide additional context without affecting primary routing.

Rules

  1. Maximum 3 Tags: No more than 3 secondary tags per classification
  2. No Primary Duplicate: Secondary tags cannot duplicate primary category
  3. Related Categories Only: Secondary tags must be from the fixed category list
  4. Relevance Threshold: Only add tags with >50% keyword match confidence

Secondary Tag Selection Process

  1. Identify all categories with keyword matches (excluding primary)
  2. Calculate relevance score for each (0.0-1.0)
  3. Sort by relevance score (descending)
  4. Select top 3 with score > 0.5
  5. Add to ClassificationResult

When to Apply Secondary Tags

  • Input contains multiple distinct sub-tasks
  • Input references multiple technology domains
  • Input implies follow-up work in other categories
  • Input has context from previous interactions in different categories

When NOT to Apply Secondary Tags

  • Input is single-focused with no additional context
  • Relevance scores are below threshold (0.5)
  • Would duplicate primary category
  • Would exceed 3-tag limit

Secondary Tag Examples

InputPrimarySecondary Tags
"Debug the API and update the docs"DEBUGGING[DOCUMENTATION]
"Refactor the auth module and add tests"REFACTORING[TESTING]
"Analyze sales data and create a report"DATA_ANALYSIS[DOCUMENTATION, COMMUNICATION]
"Fix this bug"DEBUGGING[]

Classification Models Reference

For detailed complexity, risk, confidence, and state transition models, see classification-models.md.

For system integration, failure conditions, logging, and examples, see system-integration.md.

平台分布

OpenClaw

90.13%
按下载量换算4,822

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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