doc-condenser
**DEPRECATED** — Modern Claude models condense and summarize technical documentation natively with comparable quality. The output…
Architecture reviews across 7 dimensions (structural, scalability, enterprise readiness, performance, security, ops, data) with scored reports. Triggers on: "review architecture", "critique design", "audit system", "assess scalability", "enterprise readiness", "technical due
Claude Code
$ npx -y skills add Mathews-Tom/armory --skill architecture-reviewer --agent claude-code
Codex
$ npx skills add Mathews-Tom/armory --skill architecture-reviewer -a codex -y
Cursor
$ npx skills add Mathews-Tom/armory --skill architecture-reviewer -a cursor -y
Gemini CLI
$ npx skills add Mathews-Tom/armory --skill architecture-reviewer -a gemini-cli -y
GitHub Copilot
$ npx skills add Mathews-Tom/armory --skill architecture-reviewer -a github-copilot -y
OpenCode
$ npx skills add Mathews-Tom/armory --skill architecture-reviewer -a opencode -y
Windsurf
$ npx skills add Mathews-Tom/armory --skill architecture-reviewer -a windsurf -y
name: architecture-reviewer description: 'Architecture reviews across 7 dimensions (structural, scalability, enterprise readiness, performance, security, ops, data) with scored reports. Triggers on: "review architecture", "critique design", "audit system", "assess scalability", "enterprise readiness", "technical due diligence". NOT for diagrams, use architecture-diagram. NOT for interactively finding shallow modules or designing one module''s interface and seam, use module-deepening.' metadata: version: 1.1.2 category: review tags: [architecture, scalability, enterprise, security-audit] difficulty: advanced phase: review
Systematic, framework-driven architecture review skill. Acts as a senior staff/principal engineer performing a thorough architecture critique. Not a rubber-stamp — the skill is opinionated, identifies real risks, and challenges assumptions. Every finding is tied to a concrete impact and a concrete recommendation.
The review proceeds in 4 phases:
1. **Input Classification & Context Gathering** — Determine review mode, scan inputs, ask clarifying questions (always). 2. **Dimension-by-Dimension Analysis** — Evaluate 7 dimensions, loading each reference as needed. 3. **Cross-Cutting Analysis** — Identify conflicts, coherence issues, and systemic risks. 4. **Scoring & Report Generation** — Compute scores, prioritize recommendations, produce report.
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**These constraints are NON-NEGOTIABLE. Memorize before starting any review.**
SCORE SCALE: 1-5 only (NOT 1-10, NOT percentages)
Half-scores (3.5) permitted with justification
SEVERITY LABELS: [S1] Critical — System will fail or is exploitable
[S2] High — Significant risk under realistic conditions
[S3] Medium — Design weakness limiting growth
[S4] Low — Suboptimal but manageable
[S5] Info — Best practice suggestion (also used for strengths)
DIMENSION WEIGHTS:
Structural Integrity: 20% | Performance: 17%
Scalability: 18% | Enterprise Readiness: 15%
Security: 18% | Operational Excellence: 7%
| Data Architecture: 5%
GRADE BOUNDARIES:
A = 90-100% | B = 80-89% | C = 70-79% | D = 60-69% | F = <60%
FORMULA: Overall% = (Σ dimension_score × weight) / 5 × 100**Template compliance is mandatory.** See Phase 4 checklist before finalizing any report.
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Before classifying the input, check for repo-local agent context when reviewing a codebase:
Use glossary terms in findings and recommendations. Treat ADRs as constraints unless the observed friction is severe enough to justify reopening the decision. If context files are absent, continue with inferred vocabulary.
Determine the review mode from what the user provides:
diagrams, or verbal system descriptions. No codebase available.
**If Mode A or C (codebase available):** Run the scan script to get a structural fingerprint:
bash scripts/scan_codebase.sh <codebase_path>
Review the output to understand tech stack, service boundaries, infrastructure patterns, and key configuration files before proceeding.
**If Mode B or C (documents available):** Read all provided documents. Extract:
Always ask clarifying questions before starting the analysis. Tailor questions based on what is already known from the input, but always cover these areas:
**System Context:**
**Scale & Performance Expectations:**
**Deployment & Operations:**
**Compliance & Security:**
**Scope & Focus:**
Adapt the questions — skip what's already answered by the input, and add domain-specific
Curated, production-grade skills, agents, hooks, rules, commands, utilities, and presets for AI coding agents. No magic, no demos — battle-tested workflows built for developers who use AI seriously.
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