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Agent Skill · 研究检索

planning-code-goal(规划代码目标)

planning-code-goal 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。

planning-code-goal 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,权限为需要联网;许可证为 MIT,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 54,512。

总安装 509周安装 21GitHub Stars 8下载 166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

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:planning-code-goal(规划代码目标)
来源仓库:https://github.com/vamseeachanta/workspace-hub
仓库路径:skills/planning-code-goal
安装命令:
npx skills add https://github.com/vamseeachanta/workspace-hub --skill planning-code-goal
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/vamseeachanta/workspace-hub --skill planning-code-goal

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。

planning-code-goal 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于根据关键词、任务场景或来源线索进行信息检索与筛选的场景。通过 npx skills add 命令从指定 GitHub 仓库安装并使用该技能。

安装前建议确认权限范围、维护状态及是否触发联网或文件操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/planning-code-goal

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/vamseeachanta/workspace-hub --skill planning-c…;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

    它需要需要联网,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

planning-code-goal 怎么安装?

在终端执行 npx skills add https://github.com/vamseeachanta/workspace-hub --skill planning-code-goal。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

planning-code-goal 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。

planning-code-goal 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

planning-code-goal 是免费的吗?

来源标注的许可证是 MIT。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。

planning-code-goal 用的人多吗?

公开数据是累计安装 509 次,近一周安装 21 次,下载 166 次,GitHub 8 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。

planning-code-goal 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,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 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。
  • 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
planning-code-goal

规划代码目标 · 本页

5098需要联网MIT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

Code-Centric Goal-Oriented Action Planning

SPARC-integrated planning for software development objectives with measurable outcomes

Quick Start

# Define code goal
Goal: Implement OAuth2 authentication

# SPARC-GOAP generates phased plan:
Phase 1 (Specification): Define requirements, acceptance criteria
Phase 2 (Pseudocode): Design algorithms, state machines
Phase 3 (Architecture): Design components, API contracts
Phase 4 (Refinement): TDD implementation cycles
Phase 5 (Completion): Integration, validation, deployment

# Execute with SPARC commands

When to Use

  • Feature implementation requiring systematic breakdown
  • Performance optimization with measurable targets
  • Testing strategy development with coverage goals
  • API development with clear contract definitions
  • Database evolution with migration planning
  • Technical debt reduction with incremental milestones

Prerequisites

  • Understanding of SPARC methodology phases
  • Clear definition of desired outcome
  • Access to codebase for state analysis
  • Measurable success criteria

Core Concepts

SPARC Phases in Goal Planning

PhaseGOAP RoleDeliverables
SpecificationDefine goal stateRequirements, acceptance criteria
PseudocodePlan actionsAlgorithms, state transitions
ArchitectureStructure solutionComponents, interfaces
RefinementIterate with TDDTests, implementation
CompletionValidate goalDeployment, metrics

Code State Analysis

current_state = {
  test_coverage: 45,
  performance_score: 'C',
  tech_debt_hours: 120,
  features_complete: ['auth', 'user-mgmt'],
  bugs_open: 23
}

goal_state = {
  test_coverage: 80,
  performance_score: 'A',
  tech_debt_hours: 40,
  features_complete: [...current, 'payments', 'notifications'],
  bugs_open: 5
}

Milestone Definition

interface CodeMilestone {
  id: string;
  description: string;
  sparc_phase: 'specification' | 'pseudocode' | 'architecture' | 'refinement' | 'completion';
  preconditions: string[];
  deliverables: string[];
  success_criteria: Metric[];
  estimated_hours: number;
  dependencies: string[];
}

Implementation Pattern

class SPARCGoalPlanner {
  async achieveGoal(goal: CodeGoal): Promise<GoalResult> {
    // 1. SPECIFICATION: Define goal state
    const spec = await this.specifyGoal(goal);

    // 2. PSEUDOCODE: Plan action sequence
    const actionPlan = await this.planActions(spec);

    // 3. ARCHITECTURE: Structure solution
    const architecture = await this.designArchitecture(actionPlan);

    // 4. REFINEMENT: Iterate with TDD
    const implementation = await this.refineWithTDD(architecture);

    // 5. COMPLETION: Validate and deploy
    return await this.completeGoal(implementation, spec);
  }

  async findOptimalPath(
    currentState: CodeState,
    goalState: CodeState
  ): Promise<ActionPlan> {
    const actions = this.getAvailableSPARCActions();
    return this.aStarSearch(currentState, goalState, actions);
  }
}

Configuration

sparc_goap_config:
  phases:
    specification:
      timeout_minutes: 30

    architecture:
      timeout_minutes: 45

    refinement:
      timeout_minutes: 120

    completion:
      timeout_minutes: 60

  metrics:
    test_coverage_target: 80
    performance_target: "A"
    max_tech_debt_hours: 40

  risk_assessment:
    technical_weight: 0.3
    timeline_weight: 0.3
    quality_weight: 0.2
    security_weight: 0.2

Usage Examples

Example 1: Feature Implementation Plan

goal: implement_payment_processing_with_sparc

sparc_phases:
  specification:
    deliverables:
      - requirements_doc
      - acceptance_criteria
      - test_scenarios
    success_criteria:
      - all_payment_types_defined
      - security_requirements_clear
      - compliance_standards_identified

  pseudocode:
    deliverables:
      - payment_flow_logic
      - error_handling_patterns
      - state_machine_design

  architecture:
    deliverables:
      - system_components
      - api_contracts
      - database_schema

  refinement:
    deliverables:
      - unit_tests
      - integration_tests
      - implemented_features
    success_criteria:
      - test_coverage_80_percent
      - all_tests_passing

  completion:
    deliverables:
      - deployed_system
      - documentation
      - monitoring_setup

goap_milestones:
  - setup_payment_provider:
      sparc_phase: specification
      preconditions: [api_keys_configured]
      deliverables: [provider_client, test_environment]
      success_criteria: [can_create_test_charge]

  - implement_checkout_flow:
      sparc_phase: refinement
      preconditions: [payment_provider_ready, ui_framework_setup]
      deliverables: [checkout_component, payment_form]
      success_criteria: [form_validation_works, ui_responsive]

  - add_webhook_handling:
      sparc_phase: completion
      preconditions: [server_endpoints_available]
      deliverables: [webhook_endpoint, event_processor]
      success_criteria: [handles_all_event_types, idempotent_processing]

Example 2: Performance Optimization Goal

goal: reduce_api_latency_50_percent

analysis:
  - profile_current_performance:
      tools: [profiler, APM, database_explain]
      metrics: [p50_latency, p99_latency, throughput]

optimizations:
  - database_query_optimization:
      sparc_phase: refinement
      actions: [add_indexes, optimize_joins, implement_pagination]
      expected_improvement: 30%
      success_metric: "p99 < 100ms"

  - implement_caching_layer:
      sparc_phase: architecture
      actions: [redis_setup, cache_warming, invalidation_strategy]
      expected_improvement: 25%

  - code_optimization:
      sparc_phase: refinement
      actions: [algorithm_improvements, parallel_processing, batch_operations]
      expected_improvement: 15%

Example 3: Testing Strategy Goal

goal: achieve_80_percent_coverage
current_coverage: 45

test_pyramid:
  unit_tests:
    target: 60%
    sparc_phase: refinement
    focus: [business_logic, utilities, validators]

  integration_tests:
    target: 25%
    sparc_phase: completion
    focus: [api_endpoints, database_operations, external_services]

  e2e_tests:
    target: 15%
    sparc_phase: completion
    focus: [critical_user_journeys, payment_flow, authentication]

milestones:
  - milestone_55:
      actions: [add_unit_tests_for_core_services]
      deadline: "week 1"

  - milestone_65:
      actions: [add_integration_tests_for_api]
      deadline: "week 2"

  - milestone_80:
      actions: [add_e2e_tests, increase_unit_coverage]
      deadline: "week 3"

Execution Checklist

  • Analyze current code state (coverage, performance, debt)
  • Define goal state with measurable criteria
  • Map goal to SPARC phases
  • Generate GOAP milestones for each phase
  • Estimate effort and dependencies
  • Execute SPARC commands for each phase
  • Track metrics throughout execution
  • Validate goal achievement with success criteria
  • Document patterns for future goals

Best Practices

  • Measurable Goals: Every goal needs quantifiable success criteria
  • Phase Alignment: Map GOAP actions to appropriate SPARC phases
  • TDD Integration: Use refinement phase for test-first development
  • Incremental Progress: Track metrics at each milestone
  • Risk Assessment: Evaluate technical, timeline, quality, security risks
  • Pattern Learning: Store successful plans for reuse

Error Handling

Goal Infeasibility

// Goal cannot be achieved with available resources
if (!canAchieveGoal(currentState, goalState, constraints)) {
  // Suggest achievable subset
  const achievableGoal = findMaximalAchievableSubset(goalState);
  console.log(`Full goal not achievable. Suggested: ${achievableGoal}`);

  // Identify blocking constraints
  const blockers = identifyBlockers(goalState);
  console.log(`Blocked by: ${blockers}`);
}

Phase Failures

// SPARC phase did not complete successfully
if (phaseResult.failed) {
  // Identify specific failures
  const failures = phaseResult.failedCriteria;

  // Attempt retry with adjusted parameters
  if (canRetry(failures)) {
    await retryPhase(phase, adjustedConfig);
  } else {
    // Replan from current state
    await replanFromPhase(phase);
  }
}

Metrics & Success Criteria

Code Quality Metrics

MetricTargetMeasurement
Cyclomatic Complexity< 10Per function
Code Duplication< 3%Codebase-wide
Test Coverage> 80%Line coverage
Technical Debt Ratio< 5%SonarQube

Performance Metrics

MetricTargetMeasurement
Response Time (p99)< 200msAPM
Throughput> 1000 req/sLoad test
Error Rate< 0.1%Monitoring
Availability> 99.9%Uptime

Delivery Metrics

MetricTargetMeasurement
Lead Time< 1 dayDeploy tracking
Deploy Frequency> 1/dayCI/CD
MTTR< 1 hourIncident tracking
Change Failure Rate< 5%Rollback rate

Integration Points

MCP Tools

// Initialize SPARC-enhanced swarm
  topology: "hierarchical",
  maxAgents: 5
});

// Spawn SPARC-specific agents
  type: "sparc-coder",
  capabilities: ["specification", "pseudocode", "architecture", "refinement", "completion"]
});

// Orchestrate development tasks
  task: "implement_oauth_system",
  strategy: "adaptive",
  priority: "high"
});

// Store successful patterns
  action: "store",
  namespace: "code-patterns",
  key: "oauth_implementation_plan",
  value: JSON.stringify(successfulPlan)
});

SPARC Commands

# Full SPARC-GOAP workflow

# Batch processing

Related Skills

References

Version History

  • 1.0.0 (2026-01-02): Initial release - converted from code-goal-planner agent

平台分布

Claude Code

31%
按下载量换算51

OpenCode

23.79%
按下载量换算39

Gemini CLI

17.59%
按下载量换算29

windsurf

11.64%
按下载量换算19

trae

8.46%
按下载量换算14

Cursor

3.49%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。

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