consistency-analysis
npx machina-cli add skill datamaker-kr/synapse-claude-marketplace/consistency-analysis --openclawConsistency Analysis Skill
Purpose
This skill provides expertise in detecting inconsistencies, gaps, and conflicts across specification documents. It performs read-only cross-document analysis to ensure that requirements, tasks, data models, and plans are aligned and complete. The output is a structured analysis report with categorized findings and severity levels.
IMPORTANT: This skill NEVER modifies files. It is strictly a read-only analysis tool. All findings are reported for human review and resolution.
When It Activates
The skill is triggered when the conversation involves:
- Reviewing specification quality or completeness
- Checking cross-document consistency (spec vs. tasks vs. plan)
- Detecting gaps in requirement coverage or task traceability
- Identifying conflicting definitions across documents
- Performing traceability analysis between artifacts
Analysis Rule Categories
The skill evaluates documents against eight rule categories:
1. Requirement Coverage
Verify that every functional requirement (FR-XXX) and non-functional requirement (NFR-XXX) in the specification is addressed by at least one implementation task or plan item. Flag orphaned requirements with no downstream mapping.
2. Task Traceability
Ensure every task in tasks.md traces back to a specific requirement or user story in spec.md. Flag tasks with missing [Spec §X.Y] references or invalid reference targets.
3. Plan Alignment
Check that the implementation plan is consistent with the task list and specification. Detect mismatches in scope, ordering assumptions, or phase assignments between documents.
4. Data Model Consistency
Verify that entity definitions, field names, types, and relationships are consistent across the specification, data model section, and API contracts. Flag naming mismatches, type conflicts, or missing fields.
5. Contract Coverage
Ensure every API endpoint defined in the specification has corresponding tasks for implementation and testing. Flag endpoints missing from the task list or with incomplete request/response schema definitions.
6. Constitution Compliance
If a project constitution exists, verify that the specification and tasks comply with its architectural principles, technology constraints, and coding conventions.
7. Duplication Detection
Identify duplicate or near-duplicate requirements, tasks, or definitions across documents. Flag redundancies that may lead to conflicting implementations or wasted effort.
8. Ambiguity Detection
Scan for vague, unmeasurable, or subjective language in requirements (e.g., "fast," "user-friendly," "scalable"). Flag ambiguous terms that need quantification or clarification.
Severity Classification
Each finding is assigned a severity level:
| Severity | Meaning | Action Required |
|---|---|---|
| CRITICAL | Blocking issue that prevents correct implementation | Must resolve before proceeding |
| HIGH | Significant gap or conflict likely to cause defects | Should resolve before implementation |
| MEDIUM | Inconsistency that may cause confusion or rework | Resolve during implementation |
| LOW | Minor style or convention issue | Resolve at convenience |
Report Output Format
The analysis report is structured as follows:
# Consistency Analysis Report
## Summary
- Total findings: N
- Critical: N | High: N | Medium: N | Low: N
## Findings
### [CRITICAL] RC-001: FR-012 has no implementing task
- **Category**: Requirement Coverage
- **Location**: spec.md FR-012
- **Details**: Payment retry logic requirement has no corresponding task in tasks.md.
- **Recommendation**: Add a task in the Stories phase covering FR-012.
### [HIGH] TT-001: Task T008 references non-existent FR-099
- **Category**: Task Traceability
- **Location**: tasks.md T008
- **Details**: The [Spec FR-099] reference does not match any requirement.
- **Recommendation**: Correct the reference or add the missing requirement.
Each finding includes an ID, category, location, detailed description, and a recommended resolution.
References
For detailed analysis rules, severity definitions, and configuration options, consult:
references/analysis-rules.md-- Full rule definitions with detection logic for each categoryreferences/severity-levels.md-- Severity classification criteria and escalation guidelines
Source
git clone https://github.com/datamaker-kr/synapse-claude-marketplace/blob/main/plugins/speckit-helper/skills/consistency-analysis/SKILL.mdView on GitHub Overview
Consistency Analysis identifies inconsistencies, gaps, and conflicts across specification documents. It performs read-only cross-document checks to ensure requirements, tasks, data models, and plans are aligned and complete. The results are a structured report with categorized findings and severity levels to guide human review.
How This Skill Works
The tool reads multiple artifacts (e.g., spec.md, tasks.md, data model, plan) and evaluates them against eight rule categories, including Requirement Coverage, Task Traceability, Plan Alignment, Data Model Consistency, Contract Coverage, Constitution Compliance, Duplication Detection, and Ambiguity Detection. It assigns a severity to each finding and outputs a read-only Consistency Analysis Report for human review.
When to Use It
- Reviewing specification quality or completeness
- Checking cross-document consistency (spec vs. tasks vs. plan)
- Detecting gaps in requirement coverage or task traceability
- Identifying conflicting definitions across documents
- Performing traceability analysis between artifacts
Quick Start
- Step 1: Gather spec.md, tasks.md, plan, data model, and API contract documents
- Step 2: Run Consistency Analysis to generate the findings report
- Step 3: Review high-severity findings, assign owners, and update documentation accordingly
Best Practices
- Run the analysis after drafting specs and tasks to catch gaps early
- Ensure every FR/NFR is mapped to at least one task or plan item
- Require valid cross-references (e.g., [Spec §X.Y]) in all tasks
- Keep data models and API contracts synchronized across documents
- Prioritize and resolve high-severity findings (CRITICAL/HIGH) before implementation
Example Use Cases
- [CRITICAL] FR-012 has no implementing task in the task list
- Data model name mismatches between spec.md and the data model section
- API endpoint exists in spec but lacks corresponding task or tests
- Ambiguity like 'fast' or 'scalable' without measurable criteria in FRs
- Duplicate requirements across sections leading to conflicting implementations