Install
$ agentstack add skill-patonkikh-apes-solution-architecture ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Solution Architecture
Purpose
Design a solution architecture from PRD and constraints: identify components, integration patterns, technology options, and quality attribute trade-offs.
Input: PRD or requirements, NFRs, constraints (cloud, team, budget, compliance) Output: Solution Architecture document with component model, integration view, and technology recommendations References: See [references.md](references.md) for domain cheat sheets. Examples: See [examples.md](examples.md) for worked input/output.
Workflow
Step 1: Extract architectural drivers
From input, identify:
- Functional capabilities (from FRs)
- Quality attributes (performance, security, scalability, availability, maintainability)
- Constraints (must-use tech, regulatory, timeline, team skills)
- Assumptions and unknowns
Step 2: Define architectural principles
Establish 3–5 decision principles for this solution:
- Example: "Prefer managed services over self-hosted for MVP"
- Example: "Event-driven for async workflows; sync API for user-facing reads"
Each principle must guide trade-off decisions.
Step 3: Propose logical component model
Decompose into logical components:
| Component | Responsibility | Interfaces | |-----------|----------------|------------|
Group by capability, not by team or org chart.
Step 4: Select integration patterns
For each component interaction, specify:
- Pattern: sync REST, async messaging, event bus, batch, shared DB (discourage)
- Data flow direction
- Failure handling approach
Step 5: Evaluate technology options
For each major component, provide 2–3 options with trade-off table:
| Option | Pros | Cons | Fit score |
Recommend one option with rationale tied to architectural drivers.
Step 6: Document risks and open decisions
List architectural risks and decisions requiring ADR.
Step 7: Validate
Run Validation checklist.
Decision Rules
| Condition | Action | |-----------|--------| | No PRD or requirements | Stop; request PRD or run product/prd-generator | | NFRs missing | Add minimum NFRs (security, availability, performance) before design | | Single obvious architecture | Still document rejected alternatives with rationale | | Constraint conflicts with recommendation | Flag conflict; present compliant alternative | | Premature technology choice in PRD | Separate product requirements from architecture decisions |
Validation
- [ ] Architectural drivers documented (FRs + NFRs + constraints)
- [ ] 3–5 architectural principles defined
- [ ] Component model covers all Must-have FRs
- [ ] Integration pattern specified for each interaction
- [ ] Technology options include trade-off rationale
- [ ] Risks and open decisions listed
- [ ] No implementation code or detailed API specs (defer to api-designer)
Anti-patterns
- Big ball of mud — single monolithic component without boundaries.
- Resume-driven architecture — choosing tech for novelty, not drivers.
- Shared database integration — components coupled through one DB without justification.
- Ignoring NFRs — functional-only architecture.
- Copy-paste reference architecture — applying microservices without scale justification.
Best Practices
- Align with C4 model progression: solution → context → container.
- Document rejected alternatives (supports future ADRs).
- Map each component to FR IDs for traceability.
- Prefer simplest architecture that meets NFRs (YAGNI).
- Flag decisions needing ADR before implementation.
Output Structure
# Solution Architecture: [System Name]
## Architectural Drivers
| Driver | Source | Priority |
|--------|--------|----------|
## Principles
| # | Principle | Implication |
|---|-----------|-------------|
## Component Model
| Component | Responsibility | FRs covered |
|-----------|----------------|-------------|
## Integration View
| From | To | Pattern | Data | Failure handling |
|------|----|---------|------|------------------|
## Technology Recommendations
| Component | Recommended | Alternatives | Rationale |
|-----------|-------------|--------------|-----------|
## Quality Attribute Analysis
| Attribute | Approach | Risk |
|-----------|----------|------|
## Open Decisions
| Decision | Options | Recommended ADR |
|----------|---------|-----------------|
## Risks
| Risk | Impact | Mitigation |
|------|--------|------------|
Next Skills
| Outcome | Recommended Skill | |---------|-------------------| | Document context diagram | architecture/system-context-builder | | Design APIs | architecture/api-designer | | Record key decisions | architecture/adr-generator | | Review architecture | architecture/architecture-review | | PRD missing | product/prd-generator |
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: patonkikh
- Source: patonkikh/APES
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.