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verify验证

Agent Skill

verify 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。

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808

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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

3

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:verify(验证)
来源仓库:https://github.com/yonatangross/skillforge-claude-plugin
仓库路径:skills/verify
安装命令:
npx skills add https://github.com/yonatangross/skillforge-claude-plugin --skill verify
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/yonatangross/skillforge-claude-plugin --skill verify

简介

verify 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搜集与整理的研究检索场景。
  • 通过 npx skills add 命令从 GitHub 仓库安装并使用。
  • 安装前需确认权限范围、维护状态,注意是否触发联网、命令执行或文件读写操作。
  • 建议结合原始 README 和仓库内容进一步核验具体用法和功能边界。

SKILL.md

Verify Feature

Comprehensive verification using parallel specialized agents with nuanced grading (0-10 scale) and improvement suggestions.

Quick Start

/ork:verify authentication flow
/ork:verify --model=opus user profile feature
/ork:verify --scope=backend database migrations

Argument Resolution

SCOPE = "$ARGUMENTS"       # Full argument string, e.g., "authentication flow"
SCOPE_TOKEN = "$ARGUMENTS[0]"  # First token for flag detection (e.g., "--scope=backend")
# $ARGUMENTS[0], $ARGUMENTS[1] etc. for indexed access (CC 2.1.59)

# Model override detection (CC 2.1.72)
MODEL_OVERRIDE = None
for token in "$ARGUMENTS".split():
    if token.startswith("--model="):
        MODEL_OVERRIDE = token.split("=", 1)[1]  # "opus", "sonnet", "haiku"
        SCOPE = SCOPE.replace(token, "").strip()

Pass MODEL_OVERRIDE to all Agent() calls via model=MODEL_OVERRIDE when set. Accepts symbolic names (opus, sonnet, haiku) or full IDs (claude-opus-4-6) per CC 2.1.74.

Opus 4.6: Agents use native adaptive thinking (no MCP sequential-thinking needed). Extended 128K output supports comprehensive verification reports.

STEP 0: Effort-Aware Verification Scaling (CC 2.1.76)

Scale verification depth based on /effort level:

Effort LevelPhases RunAgentsOutput
lowRun tests only → pass/fail0 agentsQuick check
mediumTests + code quality + security3 agentsScore + top issues
high (default)All 8 phases + visual capture6-7 agentsFull report + grades
Override: Explicit user selection (e.g., "Full verification") overrides /effort downscaling.

STEP 0a: Verify User Intent with AskUserQuestion

BEFORE creating tasks, clarify verification scope:

AskUserQuestion(
  questions=[{
    "question": "What scope for this verification?",
    "header": "Scope",
    "options": [
      {"label": "Full verification (Recommended)", "description": "All tests + security + code quality + visual + grades", "markdown": "```\nFull Verification (10 phases)\n─────────────────────────────\n  7 parallel agents:\n  ┌────────────┐ ┌────────────┐\n  │ Code       │ │ Security   │\n  │ Quality    │ │ Auditor    │\n  ├────────────┤ ├────────────┤\n  │ Test       │ │ Backend    │\n  │ Generator  │ │ Architect  │\n  ├────────────┤ ├────────────┤\n  │ Frontend   │ │ Performance│\n  │ Developer  │ │ Engineer   │\n  ├────────────┤ └────────────┘\n  │ Visual     │\n  │ Capture    │ → gallery.html\n  └────────────┘\n         ▼\n    Composite Score (0-10)\n    8 dimensions + Grade\n    + Visual Gallery\n```"},
      {"label": "Tests only", "description": "Run unit + integration + e2e tests", "markdown": "```\nTests Only\n──────────\n  npm test ──▶ Results\n  ┌─────────────────────┐\n  │ Unit tests     ✓/✗  │\n  │ Integration    ✓/✗  │\n  │ E2E            ✓/✗  │\n  │ Coverage       NN%  │\n  └─────────────────────┘\n  Skip: security, quality, UI\n  Output: Pass/fail + coverage\n```"},
      {"label": "Security audit", "description": "Focus on security vulnerabilities", "markdown": "```\nSecurity Audit\n──────────────\n  security-auditor agent:\n  ┌─────────────────────────┐\n  │ OWASP Top 10       ✓/✗ │\n  │ Dependency CVEs    ✓/✗ │\n  │ Secrets scan       ✓/✗ │\n  │ Auth flow review   ✓/✗ │\n  │ Input validation   ✓/✗ │\n  └─────────────────────────┘\n  Output: Security score 0-10\n          + vulnerability list\n```"},
      {"label": "Code quality", "description": "Lint, types, complexity analysis", "markdown": "```\nCode Quality\n────────────\n  code-quality-reviewer agent:\n  ┌─────────────────────────┐\n  │ Lint errors         N   │\n  │ Type coverage       NN% │\n  │ Cyclomatic complex  N.N │\n  │ Dead code           N   │\n  │ Pattern violations  N   │\n  └─────────────────────────┘\n  Output: Quality score 0-10\n          + refactor suggestions\n```"},
      {"label": "Quick check", "description": "Just run tests, skip detailed analysis", "markdown": "```\nQuick Check (~1 min)\n────────────────────\n  Run tests ──▶ Pass/Fail\n\n  Output:\n  ├── Test results\n  ├── Build status\n  └── Lint status\n  No agents, no grading,\n  no report generation\n```"}
    ],
    "multiSelect": true
  }]
)

Based on answer, adjust workflow:

  • Full verification: All 10 phases (8 + 2.5 + 8.5), 7 parallel agents including visual capture
  • Tests only: Skip phases 2 (security), 5 (UI/UX analysis)
  • Security audit: Focus on security-auditor agent
  • Code quality: Focus on code-quality-reviewer agent
  • Quick check: Run tests only, skip grading and suggestions

STEP 0b: Select Orchestration Mode

Load details: Read("${CLAUDE_SKILL_DIR}/references/orchestration-mode.md") for env var check logic, Agent Teams vs Task Tool comparison, and mode selection rules.

Choose Agent Teams (mesh -- verifiers share findings) or Task tool (star -- all report to lead) based on the orchestration mode reference.


MCP Probe + Resume

ToolSearch(query="select:mcp__memory__search_nodes")
Write(".claude/chain/capabilities.json", { memory, timestamp })

Read(".claude/chain/state.json")  # resume if exists

Handoff File

After verification completes, write results:

Write(".claude/chain/verify-results.json", JSON.stringify({
  "phase": "verify", "skill": "verify",
  "timestamp": now(), "status": "completed",
  "outputs": {
    "tests_passed": N, "tests_failed": N,
    "coverage": "87%", "security_scan": "clean"
  }
}))

Regression Monitor (CC 2.1.71)

Optionally schedule post-verification monitoring:

# Guard: Skip cron in headless/CI (CLAUDE_CODE_DISABLE_CRON)
# if env CLAUDE_CODE_DISABLE_CRON is set, run a single check instead
CronCreate(
  schedule="0 8 * * *",
  prompt="Daily regression check: npm test.
    If 7 consecutive passes → CronDelete.
    If failures → alert with details."
)

Task Management (CC 2.1.16)

# 1. Create main verification task
TaskCreate(
  subject="Verify [feature-name] implementation",
  description="Comprehensive verification with nuanced grading",
  activeForm="Verifying [feature-name] implementation"
)

# 2. Create subtasks for 8-phase process
TaskCreate(subject="Run code quality checks", activeForm="Running quality checks")    # id=2
TaskCreate(subject="Execute security audit", activeForm="Running security audit")     # id=3
TaskCreate(subject="Verify test coverage", activeForm="Verifying test coverage")      # id=4
TaskCreate(subject="Validate API", activeForm="Validating API")                       # id=5
TaskCreate(subject="Check UI/UX", activeForm="Checking UI/UX")                       # id=6
TaskCreate(subject="Calculate grades", activeForm="Calculating grades")               # id=7
TaskCreate(subject="Generate suggestions", activeForm="Generating suggestions")       # id=8
TaskCreate(subject="Compile report", activeForm="Compiling report")                   # id=9

# 3. Set dependencies — phases 2-6 run in parallel, 7-9 are sequential
TaskUpdate(taskId="7", addBlockedBy=["2", "3", "4", "5", "6"])  # Grading needs all checks
TaskUpdate(taskId="8", addBlockedBy=["7"])  # Suggestions need grades
TaskUpdate(taskId="9", addBlockedBy=["8"])  # Report needs suggestions

# 4. Before starting each task, verify it's unblocked
task = TaskGet(taskId="2")  # Verify blockedBy is empty

# 5. Update status as you progress
TaskUpdate(taskId="2", status="in_progress")  # When starting
TaskUpdate(taskId="2", status="completed")    # When done — repeat for each subtask

8-Phase Workflow

Load details: Read("${CLAUDE_SKILL_DIR}/references/verification-phases.md") for complete phase details, agent spawn definitions, Agent Teams alternative, and team teardown.

PhaseActivitiesOutput
1. Context GatheringGit diff, commit historyChanges summary
2. Parallel Agent Dispatch6 agents evaluate0-10 scores
2.5 Visual CaptureScreenshot routes, AI vision evalGallery + visual score
3. Test ExecutionBackend + frontend testsCoverage data
4. Nuanced GradingComposite score calculationGrade (A-F)
5. Improvement SuggestionsEffort vs impact analysisPrioritized list
6. Alternative ComparisonCompare approaches (optional)Recommendation
7. Metrics TrackingTrend analysisHistorical data
8. Report CompilationEvidence artifacts + gallery.htmlFinal report
8.5 Agentation LoopUser annotates, ui-feedback fixesBefore/after diffs

Phase 2 Agents (Quick Reference)

AgentFocusOutput
code-quality-reviewerLint, types, patternsQuality 0-10
security-auditorOWASP, secrets, CVEsSecurity 0-10
test-generatorCoverage, test qualityCoverage 0-10
backend-system-architectAPI design, asyncAPI 0-10
frontend-ui-developerReact 19, Zod, a11yUI 0-10
python-performance-engineerLatency, resources, scalingPerformance 0-10

Launch ALL agents in ONE message with run_in_background=True and max_turns=25.

Progressive Output (CC 2.1.76)

Output each agent's score as soon as it completes — don't wait for all 6-7 agents:

Security:     8.2/10 — No critical vulnerabilities found
Code Quality: 7.5/10 — 3 complexity hotspots identified
[...remaining agents still running...]

This gives users real-time visibility into multi-agent verification. If any dimension scores below the security_minimum threshold (default 5.0), flag it as a blocker immediately — the user can terminate early without waiting for remaining agents.

Phase 2.5: Visual Capture (NEW — runs in parallel with Phase 2)

Load details: Read("${CLAUDE_SKILL_DIR}/references/visual-capture.md") for auto-detection, route discovery, screenshot capture, and AI vision evaluation.

Summary: Auto-detects project framework, starts dev server, discovers routes, uses agent-browser to screenshot each route, evaluates with Claude vision, generates self-contained gallery.html with base64-embedded images.

Output: verification-output/{timestamp}/gallery.html — open in browser to see all screenshots with AI evaluations, scores, and annotation diffs.

Graceful degradation: If no frontend detected or server won't start, skips visual capture with a warning — never blocks verification.

Phase 8.5: Agentation Visual Feedback (opt-in)

Load details: Read("${CLAUDE_SKILL_DIR}/references/visual-capture.md") (Phase 8.5 section) for agentation loop workflow.

Trigger: Only when agentation MCP is configured. Offers user the choice to annotate the live UI. ui-feedback agent processes annotations, re-screenshots show before/after.


Grading & Scoring

Load Read("${CLAUDE_PLUGIN_ROOT}/skills/quality-gates/references/unified-scoring-framework.md") for dimensions, weights, grade thresholds, and improvement prioritization. Load Read("${CLAUDE_SKILL_DIR}/references/quality-model.md") for verify-specific extensions (Visual dimension). Load Read("${CLAUDE_SKILL_DIR}/references/grading-rubric.md") for per-agent scoring criteria.


Evidence & Test Execution

Load details: Read("${CLAUDE_SKILL_DIR}/rules/evidence-collection.md") for git commands, test execution patterns, metrics tracking, and post-verification feedback.


Policy-as-Code

Load details: Read("${CLAUDE_SKILL_DIR}/references/policy-as-code.md") for configuration.

Define verification rules in .claude/policies/verification-policy.json:

{
  "thresholds": {
    "composite_minimum": 6.0,
    "security_minimum": 7.0,
    "coverage_minimum": 70
  },
  "blocking_rules": [
    {"dimension": "security", "below": 5.0, "action": "block"}
  ]
}

Report Format

Load details: Read("${CLAUDE_SKILL_DIR}/references/report-template.md") for full format. Summary:

# Feature Verification Report

**Composite Score: [N.N]/10** (Grade: [LETTER])

## Verdict
**[READY FOR MERGE | IMPROVEMENTS RECOMMENDED | BLOCKED]**

References

Load on demand with Read("${CLAUDE_SKILL_DIR}/references/<file>"):

FileContent
verification-phases.md8-phase workflow, agent spawn definitions, Agent Teams mode
visual-capture.mdPhase 2.5 + 8.5: screenshot capture, AI vision, gallery generation, agentation loop
quality-model.mdScoring dimensions and weights (8 unified)
grading-rubric.mdPer-agent scoring criteria
report-template.mdFull report format with visual evidence section
alternative-comparison.mdApproach comparison template
orchestration-mode.mdAgent Teams vs Task Tool
policy-as-code.mdVerification policy configuration
verification-checklist.mdPre-flight checklist

Rules

Load on demand with Read("${CLAUDE_SKILL_DIR}/rules/<file>"):

FileContent
scoring-rubric.mdComposite scoring, grades, verdicts
evidence-collection.mdEvidence gathering and test patterns

Agent Coordination

SendMessage (Cross-Agent Findings)

When a security agent finds a critical issue, share it with other verification agents:

SendMessage(to="test-generator", message="Security: SQL injection in user_service.py:88 — add parameterized query test")
SendMessage(to="code-quality-reviewer", message="Security finding at user_service.py:88 — flag in review")

Skill Chain

After verification, chain to commit if all gates pass:

TaskCreate(subject="Commit verified changes", activeForm="Committing", addBlockedBy=[verify_task_id])
# Then: /ork:commit

Related Skills

  • ork:implement - Full implementation with verification
  • ork:review-pr - PR-specific verification
  • testing-unit / testing-integration / testing-e2e - Test execution patterns
  • ork:quality-gates - Quality gate patterns
  • browser-tools - Browser automation for visual capture

Version: 4.2.0 (March 2026) — Added progressive output for incremental agent scores

适合场景

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用户想查找某类 Agent Skill 时

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需要根据任务场景推荐可安装能力包时

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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

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

Claude Code

26.73%
按下载量换算76

OpenCode

21.98%
按下载量换算62

Antigravity

15.94%
按下载量换算45

Gemini CLI

13.85%
按下载量换算39

windsurf

8.61%
按下载量换算24

trae

3.75%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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 https://github.com/yonatangross/skillforge-claude-plugin --skill verify;npx skills add yonatangross/skillforge-claude-plugin --skill "verify" 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。

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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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