Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rjmurillo/ai-agents --skill cva-analysisgit clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rjmurillo/ai-agents/cva-analysis)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/cva-analysis"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/cva-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/cva-analysis"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/cva-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.04244 |
| Opus 5.5 | $0.00023 | $0.01698 |
| Sonnet 5.5 | $0.00012 | $0.00849 |
| Haiku 4.5 | $0.00006 | $0.00424 |
Grade A, and why
cva-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 462 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CVA Analysis - Discover Natural Abstractions
Commonality Variability Analysis (CVA) is a systematic technique for discovering abstractions from requirements. Instead of choosing patterns first, you build a matrix showing what's COMMON (constant across use cases) vs what VARIES (differs between cases). Patterns emerge naturally from the matrix structure.
Core Insight: Rows (commonalities) map to Strategy pattern. Columns (variabilities) map to Abstract Factory pattern. The matrix reveals whether abstraction is needed at all.
From CLAUDE.md Design Philosophy: "Greatest vulnerability is wrong or missing abstraction." CVA prevents wrong abstractions by making pattern selection evidence-based, not intuition-based.
Triggers
Activate CVA when you encounter:
discover abstractions for {domain}run CVA analysis on {requirements}commonality variability analysisprevent wrong abstractionwhat patterns emerge from {use cases}
Quick Reference
| Phase | Purpose | Input | Output | Time |
|---|---|---|---|---|
| 1. Identify Commonalities | Find what's constant across ALL use cases | Use cases/requirements | List of commonalities (matrix rows) | 5-10 min |
| 2. Identify Variabilities | Find what VARIES between use cases | Commonalities + use cases | List of variabilities (matrix columns) | 5-10 min |
| 3. Build Matrix | Visualize commonality × variability relationships | Rows + columns | CVA matrix (Markdown table) | 5-15 min |
| 4. Map to Patterns | Translate matrix structure to design patterns | Completed matrix | Pattern recommendations + rationale | 5-10 min |
| 5. Validate & Handoff | Validate choices, route to appropriate agents | Pattern recommendations | Validation report + ADR stub | 5-10 min |
Total Time: Quick (15 min), Standard (30 min), Deep (60 min)
When to Use
Use CVA when:
- Facing 2+ similar but varying requirements
- About to select a design pattern (validate with CVA first)
- Refactoring code with duplicated logic
- Need evidence for abstraction decisions (ADR material)
- Team debates whether to abstract (make implicit explicit)
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/coplien-multi-paradigm-design.md 3.0 KB
- references/gof-pattern-selection.md 2.9 KB
- references/matrix-building-examples.md 12 KB
- references/multidimensional-cva.md 2.7 KB
- references/pattern-mapping-guide.md 10 KB
- references/SKILL_SPEC.md 38 KB
- scripts/validate-cva-matrix.py 10 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +5 lines 1fdcc2769463
- 1mo ago First seen · 457 lines · 58 tokens per session scan A 43e45215d0c6
cva-analysis is a skill published in the GitHub repository rjmurillo/ai-agents (49 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 4,244 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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