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urf乌尔夫

Agent Skill

urf 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。

总安装

343

周安装

14

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111
CodexClaudeCursorGemini CLI

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

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:urf(乌尔夫)
来源仓库:https://github.com/zpankz/mcp-skillset
仓库路径:skills/urf
安装命令:
npx skills add zpankz/mcp-skillset --skill "urf"
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。

AgentSkills.tonpx skills
npx skills add zpankz/mcp-skillset --skill "urf"

简介

urf 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。

  • 适用于研究检索类任务,支持特定领域资源查询。
  • 通过 npx skills add zpankz/mcp-skillset --skill "urf" 安装,需确认权限范围。
  • 建议安装前核实维护状态、技能功能边界及是否涉及联网或命令执行。
  • 可结合原始 README 文档进一步了解其实际应用场景和限制条件。

SKILL.md

name
urf
description
Universal Reasoning Framework implementing λο.τ calculus over holarchic structures. Provides severity-based routing (R0-R3 pipelines), modular cognitive architecture (DEC, EVL, PAT, SYN, MEA, HYP, INT), fractal execution patterns, multi-level validation (η≥4, KROG), and adaptive learning. Triggers on: (1) complex multi-step reasoning, (2) high-stakes decisions requiring validation, (3) research synthesis across domains, (4) system design and architecture, (5) crisis management, (6) performance optimization. Implements scale-invariant reasoning from micro (tool calls) through meso (skill composition) to macro (orchestrated workflows).

Universal Reasoning Framework (URF)

λο.τ Universal Form

λ : Operation     — The transformation
ο : Base          — Input entity  
τ : Terminal      — Target output

λο.τ : Base → Terminal via Operation

Composition Operators:

(∘) : sequential   — (λ₁ ∘ λ₂)ο = λ₁(λ₂(ο))
(⊗) : parallel     — (λ₁ ⊗ λ₂)ο = (λ₁(ο), λ₂(ο))
(*) : recursive    — fix(λ) = λ(fix(λ))
(|) : conditional  — (λ | c)ο = Just(λ(ο)) if c(ο) else Nothing

Π-Classification

Classify every query before execution:

ScorePipelineHolonsToolsValidationτ-Form
<2R0≤2 sentences
2-4R1{ρ}∨{θ}optionalimplicit1-2¶
4-8R2{γ,η}∪{ρ,θ}infranodusη≥4mechanistic
≥8R3Σ (all)allKROG∧η≥4∧PSRcomprehensive

Score Calculation:

score = (
    len(domains) * 2 +          # Multi-domain bonus
    reasoning_depth * 3 +       # Deep reasoning weight
    (1.5 if high_stakes else 1.0) +  # Safety multiplier
    (2 if requires_verification else 0)  # Recency/fact-check
)

Auto-Escalation Triggers:

  • Verification requests ("latest", "current", "2025") → R3
  • Trivial factual ("What is...", "Define...") → R0
  • Medical/legal stakes → score × 1.5

Σ-Complex (Module Registry)

SymbolModuleSignatureWhen to Use
ρreasonparse→branch→reduce→ground→emitAny reasoning
θthinkthoughtbox ⊗ mental_models ⊗ notebookCognitive enhancement
ωontologsimplices→homology→sheavesFormal structures
γgraphextract→compress→validate(η≥4)Knowledge graphs
ηhierarchicalstrategic→tactical→operationalMulti-scale problems
κcritiquethesis→antithesis→synthesisDialectical refinement
αagencyobserve→reason→plan→act→reflectTask execution
νnon-linearorchestrator⊗workers→checkpointUncertainty handling
βabductdetect→infer→refactor→validateSchema optimization
χconstraintsKROG: K∧R∧O∧GGovernance validation

Edge Registry (Composition Patterns):

(ρ, θ): ∘   # reason feeds think
(θ, ω): ∘   # think grounds in ontolog
(ω, ρ): ∘   # ontolog constrains reason
(γ, η): ⊗   # graph parallel hierarchical
(κ, β): ∘   # critique feeds abduct
(β, κ): *   # recursive refinement
(α, ν): ∘   # agency orchestrates non-linear
(ν, χ): |   # non-linear conditional on constraints

Ψ-Execution Patterns

R0: Direct Response

λR0 = id  # Identity transformation, <100ms

R1: Single Skill

λR1 = ρ.emit ∘ ρ.ground ∘ ρ.reduce ∘ ρ.parse
# parse→branch→reduce→ground→emit

R2: Skill Composition

λR2 = (
    validate(η≥4) ∘ 
    γ.compress ∘ 
    (γ.extract ⊗ η.decompose) ∘ 
    ρ.parse
)

R3: Full Orchestration

λR3 = (
    χ.validate(KROG) ∘
    β.refactor ∘
    κ.synthesize ∘
    (ρ ⊗ θ ⊗ ω).parallel ∘
    κ.thesis ∘
    ν.orchestrate ∘
    α.observe
)

Γ-Topology Invariants

Required Metrics:

TARGETS = {
    "η": ("|E|/|V|", "≥", 4.0),      # Density ratio
    "ζ": ("cycles", "=", 0),         # Acyclicity
    "κ": ("clustering", ">", 0.3),   # Small-world
    "φ": ("isolated", "<", 0.2),     # Connectivity
}

Validation:

def validate(graph) -> bool:
    return (
        graph.edges / graph.nodes >= 4.0 and  # η ≥ 4
        not has_cycles(graph) and              # ζ = 0
        clustering_coefficient(graph) > 0.3 and  # κ > 0.3
        isolated_ratio(graph) < 0.2            # φ < 0.2
    )

Remediation Actions:

  • η < 4: invoke infranodus:getGraphAndAdvice with optimize="gaps"
  • ζ > 0: invoke abduct.refactor with cycle_breaking=True
  • κ < 0.3: invoke graph.add_triangulation
  • φ > 0.2: invoke graph.connect_orphans

χ-Constraints (KROG Theorem)

Valid(λ) ⟺ K(λ) ∧ R(λ) ∧ O(λ) ∧ G(λ)

K (Knowable):    Effects transparent, auditable
R (Rights):      Agent has authority over domain
O (Obligations): All duties satisfied
G (Governance):  Within meta-bounds

Constraint Trichotomy:

TypeEffectRigidity
EnablingExpands action spaceDynamic
GoverningChannels possibilitiesStatic
ConstitutiveDefines identityImmutable

Execution Lifecycle

1. RECEIVE    → Parse query components
2. CLASSIFY   → Score → Pipeline selection
3. LOAD       → Memories + PKM + Context
4. ROUTE      → Activate appropriate holons
5. REASON     → Strategic→Tactical→Operational
6. GROUND     → Gather evidence, verify premises
7. COMPOSE    → Synthesize outputs from holons
8. VALIDATE   → Check invariants (η≥4, KROG)
9. SYNTHESIZE → Format per pipeline τ-form
10. PERSIST   → Update memories if new facts
11. EMIT      → Deliver response

Convergence Detection:

def converged(state, previous, pipeline) -> bool:
    similarity = (
        0.5 * cosine(state.strategic, previous.strategic) +
        0.3 * cosine(state.tactical, previous.tactical) +
        0.2 * cosine(state.operational, previous.operational)
    )
    thresholds = {R1: 0.85, R2: 0.92, R3: 0.96}
    return similarity > thresholds[pipeline]

Φ-Formatting Axioms

  1. PROSE_PRIMACY: Organic paragraphs; lists only when requested
  2. TELEOLOGY_FIRST: Why → How → What
  3. MECHANISTIC_TRACE: Explicit causal chains A → B → C
  4. UNCERTAINTY_HONEST: State confidence, acknowledge gaps
  5. MINIMAL_FORMATTING: Headers/bullets only when structurally necessary

Token Scaling:

PipelineTokensForm
R0≤501-2 sentences
R1100-3001-2 paragraphs
R2300-800Mechanistic explanation
R3500-2000Comprehensive synthesis

Integration Points

Tool Selection:

TOOL_MAP = {
    "current_info": ["exa:web_search", "scholar-gateway"],
    "graph_analysis": ["infranodus:getGraphAndAdvice"],
    "extended_reasoning": ["clear-thought", "atom-of-thoughts"],
    "workflow": ["rube", "n8n"],
    "memory": ["supermemory", "limitless"],
}

Skill Composition:

urf → hierarchical-reasoning   # Multi-level reasoning
urf → knowledge-graph          # Graph operations
urf → ontolog                  # Formal structures
urf → abduct                   # Schema optimization
urf → critique                 # Dialectical synthesis

Emergency Protocols

Severity Escalation:

DEFCON_5 (Normal):   All systems nominal
DEFCON_4 (Elevated): Minor anomalies, increased monitoring
DEFCON_3 (High):     Multiple anomalies, active mitigation
DEFCON_2 (Severe):   System-wide issues, emergency protocols
DEFCON_1 (Critical): Total failure imminent, crisis mode

Override Codes:

  • HALT: Immediate stop → Recovery procedures
  • ROLLBACK: Undo to last safe state
  • ESCALATE: Bump severity + external help
  • BYPASS: Skip validation (CRITICAL only, requires KROG override)

References

Quick Reference

λο.τ                    Universal form
η = |E|/|V| ≥ 4         Topology target
KROG = K∧R∧O∧G          Constraint validation
R0 < R1 < R2 < R3       Pipeline escalation

∘ sequential | ⊗ parallel | * recursive | | conditional

parse→branch→reduce→ground→emit     (reason)
strategic→tactical→operational       (hierarchical)
thesis→antithesis→synthesis          (critique)
observe→reason→plan→act→reflect      (agency)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

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

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

OpenCode

29.65%
按下载量换算33

Claude Code

24.06%
按下载量换算27

windsurf

17.64%
按下载量换算20

Codex

13.05%
按下载量换算14

kiro-cli

7.22%
按下载量换算8

mcpjam

3.02%
按下载量换算3

安全审计

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权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add zpankz/mcp-skillset --skill "urf" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

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来源信息

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