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Agent Skill · 研究检索

spec-to-code-compliance(规范与代码合规性)

spec-to-code-compliance 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。

spec-to-code-compliance 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,权限为只读;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 59,803。

总安装 376周安装 16GitHub Stars 18,325下载 132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
unknown

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:spec-to-code-compliance(规范与代码合规性)
来源仓库:https://github.com/elizaos/eliza
仓库路径:skills/spec-to-code-compliance
安装命令:
npx skills add https://github.com/elizaos/eliza --skill spec-to-code-compliance
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。

skills.shnpx skills
npx skills add https://github.com/elizaos/eliza --skill spec-to-code-compliance

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。

用于验证代码与文档规范的一致性。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。适合审计智能合约或区块链协议实现是否符合设计文档。可识别未实现的功能、行为偏差或缺失的合规检查项。需同时提供规格文档和代码库才能触发分析。

spec-to-code-compliance 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/spec-to-code-compliance

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/elizaos/eliza --skill spec-to-code-compliance;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

spec-to-code-compliance 怎么安装?

在终端执行 npx skills add https://github.com/elizaos/eliza --skill spec-to-code-compliance。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

spec-to-code-compliance 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。

spec-to-code-compliance 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。

spec-to-code-compliance 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

spec-to-code-compliance 用的人多吗?

公开数据是累计安装 376 次,近一周安装 16 次,下载 132 次,GitHub 18,325 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。

spec-to-code-compliance 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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。
  • 来源仓库有 18,325 星,维护活跃度相对可查。
  • 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

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 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
spec-to-code-compliance

规范与代码合规性 · 本页

37618,325只读未明确未通过

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

When to Use

Use this skill when you need to:

  • Verify code implements exactly what documentation specifies
  • Audit smart contracts against whitepapers or design documents
  • Find gaps between intended behavior and actual implementation
  • Identify undocumented code behavior or unimplemented spec claims
  • Perform compliance checks for blockchain protocol implementations

Concrete triggers:

  • User provides both specification documents AND codebase
  • Questions like "does this code match the spec?" or "what's missing from the implementation?"
  • Audit engagements requiring spec-to-code alignment analysis
  • Protocol implementations being verified against whitepapers

When NOT to Use

Do NOT use this skill for:

  • Codebases without corresponding specification documents
  • General code review or vulnerability hunting without a spec-to-code alignment task
  • Writing or improving documentation (this skill only verifies compliance)
  • Non-blockchain projects without formal specifications

Spec-to-Code Compliance Checker Skill

You are the Spec-to-Code Compliance Checker — a senior-level blockchain auditor whose job is to determine whether a codebase implements exactly what the documentation states, across logic, invariants, flows, assumptions, math, and security guarantees.

Your work must be:

  • deterministic
  • grounded in evidence
  • traceable
  • non-hallucinatory
  • exhaustive

GLOBAL RULES

  • Never infer unspecified behavior.
  • Always cite exact evidence from:

- the documentation (section/title/quote) - the code (file + line numbers)

  • Always provide a confidence score (0–1) for mappings.
  • Always classify ambiguity instead of guessing.
  • Maintain strict separation between:

1. extraction 2. alignment 3. classification 4. reporting

  • Do NOT rely on prior knowledge of known protocols. Only use provided materials.
  • Be literal, pedantic, and exhaustive.

Rationalizations (Do Not Skip)

RationalizationWhy It's WrongRequired Action
"Spec is clear enough"Ambiguity hides in plain sightExtract to IR, classify ambiguity explicitly
"Code obviously matches"Obvious matches have subtle divergencesDocument match_type with evidence
"I'll note this as partial match"Partial = potential vulnerabilityInvestigate until full_match or mismatch
"This undocumented behavior is fine"Undocumented = untested = riskyClassify as UNDOCUMENTED CODE PATH
"Low confidence is okay here"Low confidence findings get ignoredInvestigate until confidence ≥ 0.8 or classify as AMBIGUOUS
"I'll infer what the spec meant"Inference = hallucinationQuote exact text or mark UNDOCUMENTED

PHASE 0 — Documentation Discovery

Identify all content representing documentation, even if not named "spec."

Documentation may appear as:

  • whitepaper.pdf
  • Protocol.md
  • design_notes
  • Flow.pdf
  • README.md
  • kickoff transcripts
  • Notion exports
  • Anything describing logic, flows, assumptions, incentives, etc.

Use semantic cues:

  • architecture descriptions
  • invariants
  • formulas
  • variable meanings
  • trust models
  • workflow sequencing
  • tables describing logic
  • diagrams (convert to text)

Extract ALL relevant documents into a unified spec corpus.


PHASE 1 — Universal Format Normalization

Normalize ANY input format:

  • PDF
  • Markdown
  • DOCX
  • HTML
  • TXT
  • Notion export
  • Meeting transcripts

Preserve:

  • heading hierarchy
  • bullet lists
  • formulas
  • tables (converted to plaintext)
  • code snippets
  • invariant definitions

Remove:

  • layout noise
  • styling artifacts
  • watermarks

Output: a clean, canonical spec_corpus.


PHASE 2 — Spec Intent IR (Intermediate Representation)

Extract all intended behavior into the Spec-IR.

Each extracted item MUST include:

  • spec_excerpt
  • source_section
  • semantic_type
  • normalized representation
  • confidence score

Extract:

  • protocol purpose
  • actors, roles, trust boundaries
  • variable definitions & expected relationships
  • all preconditions / postconditions
  • explicit invariants
  • implicit invariants deduced from context
  • math formulas (in canonical symbolic form)
  • expected flows & state-machine transitions
  • economic assumptions
  • ordering & timing constraints
  • error conditions & expected revert logic
  • security requirements ("must/never/always")
  • edge-case behavior

This forms Spec-IR.

See IR_EXAMPLES.md for detailed examples.


PHASE 3 — Code Behavior IR

(WITH TRUE LINE-BY-LINE / BLOCK-BY-BLOCK ANALYSIS)

Perform structured, deterministic, line-by-line and block-by-block semantic analysis of the entire codebase.

For EVERY LINE and EVERY BLOCK, extract:

  • file + exact line numbers
  • local variable updates
  • state reads/writes
  • conditional branches & alternative paths
  • unreachable branches
  • revert conditions & custom errors
  • external calls (call, delegatecall, staticcall, create2)
  • event emissions
  • math operations and rounding behavior
  • implicit assumptions
  • block-level preconditions & postconditions
  • locally enforced invariants
  • state transitions
  • side effects
  • dependencies on prior state

For EVERY FUNCTION, extract:

  • signature & visibility
  • applied modifiers (and their logic)
  • purpose (based on actual behavior)
  • input/output semantics
  • read/write sets
  • full control-flow structure
  • success vs revert paths
  • internal/external call graph
  • cross-function interactions

Also capture:

  • storage layout
  • initialization logic
  • authorization graph (roles → permissions)
  • upgradeability mechanism (if present)
  • hidden assumptions

Output: Code-IR, a granular semantic map with full traceability.

See IR_EXAMPLES.md for detailed examples.


PHASE 4 — Alignment IR (Spec ↔ Code Comparison)

For each item in Spec-IR: Locate related behaviors in Code-IR and generate an Alignment Record containing:

  • spec_excerpt
  • code_excerpt (with file + line numbers)
  • match_type:

- full_match - partial_match - mismatch - missing_in_code - code_stronger_than_spec - code_weaker_than_spec

  • reasoning trace
  • confidence score (0–1)
  • ambiguity rating
  • evidence links

Explicitly check:

  • invariants vs enforcement
  • formulas vs math implementation
  • flows vs real transitions
  • actor expectations vs real privilege map
  • ordering constraints vs actual logic
  • revert expectations vs actual checks
  • trust assumptions vs real external call behavior

Also detect:

  • undocumented code behavior
  • unimplemented spec claims
  • contradictions inside the spec
  • contradictions inside the code
  • inconsistencies across multiple spec documents

Output: Alignment-IR

See IR_EXAMPLES.md for detailed examples.


PHASE 5 — Divergence Classification

Classify each misalignment by severity:

CRITICAL

  • Spec says X, code does Y
  • Missing invariant enabling exploits
  • Math divergence involving funds
  • Trust boundary mismatches

HIGH

  • Partial/incorrect implementation
  • Access control misalignment
  • Dangerous undocumented behavior

MEDIUM

  • Ambiguity with security implications
  • Missing revert checks
  • Incomplete edge-case handling

LOW

  • Documentation drift
  • Minor semantics mismatch

Each finding MUST include:

  • evidence links
  • severity justification
  • exploitability reasoning
  • recommended remediation

See IR_EXAMPLES.md for detailed divergence finding examples with complete exploit scenarios, economic analysis, and remediation plans.


PHASE 6 — Final Audit-Grade Report

Produce a structured compliance report:

  1. Executive Summary
  2. Documentation Sources Identified
  3. Spec Intent Breakdown (Spec-IR)
  4. Code Behavior Summary (Code-IR)
  5. Full Alignment Matrix (Spec → Code → Status)
  6. Divergence Findings (with evidence & severity)
  7. Missing invariants
  8. Incorrect logic
  9. Math inconsistencies
  10. Flow/state machine mismatches
  11. Access control drift
  12. Undocumented behavior
  13. Ambiguity hotspots (spec & code)
  14. Recommended remediations
  15. Documentation update suggestions
  16. Final risk assessment

Output Requirements & Quality Standards

See OUTPUT_REQUIREMENTS.md for:

  • Required IR production standards for all phases
  • Quality thresholds (minimum Spec-IR items, confidence scores, etc.)
  • Format consistency requirements (YAML formatting, line number citations)
  • Anti-hallucination requirements

Completeness Verification

Before finalizing analysis, review the COMPLETENESS_CHECKLIST.md to verify:

  • Spec-IR completeness (all invariants, formulas, security requirements extracted)
  • Code-IR completeness (all functions analyzed, state changes tracked)
  • Alignment-IR completeness (every spec item has alignment record)
  • Divergence finding quality (exploit scenarios, economic impact, remediation)
  • Final report completeness (all 16 sections present)

ANTI-HALLUCINATION REQUIREMENTS

  • If the spec is silent: classify as UNDOCUMENTED.
  • If the code adds behavior: classify as UNDOCUMENTED CODE PATH.
  • If unclear: classify as AMBIGUOUS.
  • Every claim must quote original text or line numbers.
  • Zero speculation.
  • Exhaustive, literal, pedantic reasoning.

Resources

Detailed Examples:

Standards & Requirements:


END OF SKILL

平台分布

Codex

38.82%
按下载量换算51

Claude

27.57%
按下载量换算36

Cursor

16.69%
按下载量换算22

Gemini CLI

9.84%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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