Spec To Code Compliance V2

Spec-to-Code Compliance Checker Skill

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/spec-to-code-compliance from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Rationalizations (Do Not Skip), Output Requirements & Quality Standards, Completeness Verification, Agent, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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
• User provides both specification documents AND codebase

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Rationalizations (Do Not Skip)

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

Examples

Example 1: Ask for the upstream workflow directly

Use @spec-to-code-compliance-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @spec-to-code-compliance-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @spec-to-code-compliance-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @spec-to-code-compliance-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/spec-to-code-compliance, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: 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

Imported: 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:

• IR_EXAMPLES.md - Complete IR workflow examples with DEX swap patterns

Standards & Requirements:

• OUTPUT_REQUIREMENTS.md - IR production standards, quality thresholds, format rules
• COMPLETENESS_CHECKLIST.md - Verification checklist for all phases

Imported: Agent

The spec-compliance-checker agent performs the full 7-phase specification-to-code compliance workflow autonomously. Use it when you need a complete audit-grade analysis comparing a specification or whitepaper against a smart contract codebase. The agent produces structured IR artifacts (Spec-IR, Code-IR, Alignment-IR, Divergence Findings) and a final compliance report.

Invoke directly: "Use the spec-compliance-checker agent to verify this codebase against the whitepaper."

END OF SKILL

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.