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Get Started Free →Displays the roster of architecture team members with their specialties and expertise areas. Use when the user asks "Who's on the architecture team?", "List architecture members", "Show me the architects", "What specialists are available?", "Who can I ask for reviews?", or wants to discover available experts. Do NOT use for requesting reviews (use specialist-review or architecture-review) or checking documentation status (use architecture-status).
.claude/skills/majiayu000-list-members/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 58% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 13% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 12% | 0% |
Displays all architecture team members and their expertise areas.
If .architecture/members.yml doesn't exist:
The AI Software Architect framework hasn't been set up yet.
To get started: "Setup ai-software-architect"Read .architecture/members.yml and parse all members (id, name, title, specialties, disciplines, skillsets, domains, perspective).
markdown# Architecture Team Members Your AI Software Architect team consists of [count] specialized reviewers. Total Members: [count] --- ## Team Roster ### [Member 1 Name] - [Member 1 Title] **ID**: `[member_id]` **Specialties**: [Specialty 1], [Specialty 2], [Specialty 3] **Disciplines**: [Discipline 1], [Discipline 2] **Domains**: [Domain 1], [Domain 2], [Domain 3] **Perspective**: [Their unique perspective] **Request review**: `Ask [Member Title] to review [your target]` --- [Repeat for all members] --- ## Quick Reference **Specialist reviews**: - `Ask [Specialist Title] to review [target]` **Examples**: - "Ask Security Specialist to review authentication" - "Ask Performance Specialist to review database queries" - "Ask [Your Specialist] to review [anything]" **Full architecture review**: - `Start architecture review for version X.Y.Z` **Other commands**: - `Create ADR for [decision topic]` - `What's our architecture status?` --- ## Team by Specialty [Group members by their primary domains/specialties] **Security & Compliance**: [Members] **Performance & Scalability**: [Members] **Code Quality & Maintainability**: [Members] **Domain & Business Logic**: [Members] **System Design & Architecture**: [Members] **Technology-Specific**: [Members] --- ## Adding New Members Request a review from any specialist, even if they don't exist: - "Ask Ruby Expert to review my modules" - "Have Accessibility Expert review forms" I'll create the specialist and add them to your team automatically. Or manually edit `.architecture/members.yml` and add:
name: "Name]" title: "Title]" specialties: "Specialty 1]", "Specialty 2]"] disciplines: "Discipline 1]", "Discipline 2]"] skillsets: "Skill 1]", "Skill 2]"] domains: "Domain 1]", "Domain 2]"] perspective: "Brief description]"
---
## Using the Team
**For focused reviews** (specific expertise):Ask Specialist] to review target]
Fast turnaround, targeted insights
**For comprehensive reviews** (all perspectives):Start architecture review for version/feature]
All members review, collaborative discussion
**For decisions**:Create ADR for decision topic]
Document decisions with team input
---After listing:
After showing full roster, provide a concise summary:
Ready to use your architecture team:
- [N] specialists available
- Request reviews: "Ask [specialist] to review [target]"
- Add new specialists: Just ask for them by name
- Full review: "Start architecture review for [scope]".architecture/: Offer setup instructionsmembers.yml: Show default team and offer setupAfter Listing Members:
When Adding Members:
Workflow Examples:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | fail→pass | 9,499 | 13,246 | +39% | 1 | 1 | 0% | 1,605 | 2,541 | +58% | 0 | 0 | — |
case-01 | fail→fail | 8,244 | 8,048 | -2% | 1 | 1 | 0% | 431 | 1,433 | +232% | 0 | 0 | — |
case-02 | fail→fail | 6,752 | 18,346 | +172% | 1 | 1 | 0% | 1,048 | 4,157 | +297% | 0 | 0 | — |
case-03 | fail→fail | 8,237 | 15,078 | +83% | 1 | 1 | 0% | 1,483 | 1,307 | -12% | 0 | 0 | — |
case-05 | fail→pass | 16,635 | 6,968 | -58% | 1 | 1 | 0% | 1,881 | 2,280 | +21% | 0 | 0 | — |
case-06 | fail→pass | 15,748 | 7,725 | -51% | 1 | 1 | 0% | 2,261 | 2,552 | +13% | 0 | 0 | — |
case-07 | fail→pass | 8,052 | 9,252 | +15% | 1 | 1 | 0% | 1,231 | 1,809 | +47% | 0 | 0 | — |
case-08 | pass→pass | 14,845 | 10,522 | -29% | 1 | 1 | 0% | 2,363 | 2,897 | +23% | 0 | 0 | — |
case-09 | fail→pass | 11,794 | 5,750 | -51% | 1 | 1 | 0% | 1,910 | 2,148 | +12% | 0 | 0 | — |
case-10 | fail→pass | 16,914 | 12,415 | -27% | 1 | 1 | 0% | 2,111 | 2,308 | +9% | 0 | 0 | — |
case-11 | fail→pass | 9,875 | 2,149 | -78% | 1 | 1 | 0% | 720 | 1,419 | +97% | 0 | 0 | — |
case-12 | pass→pass | 32,977 | 8,593 | -74% | 1 | 1 | 0% | 2,247 | 1,695 | -25% | 0 | 0 | — |
case-13 | fail→fail | 9,237 | 3,513 | -62% | 1 | 1 | 0% | 1,482 | 1,722 | +16% | 0 | 0 | — |
case-14 | fail→pass | 10,329 | 2,952 | -71% | 1 | 1 | 0% | 1,897 | 1,634 | -14% | 0 | 0 | — |
case-15 | fail→pass | 2,362 | 4,089 | +73% | 1 | 1 | 0% | 347 | 1,981 | +471% | 0 | 0 | — |
case-16 | pass→pass | 9,481 | 10,316 | +9% | 1 | 1 | 0% | 1,449 | 2,196 | +52% | 0 | 0 | — |
case-17 | pass→pass | 9,470 | 8,359 | -12% | 1 | 1 | 0% | 1,659 | 2,460 | +48% | 0 | 0 | — |
case-18 | pass→pass | 21,948 | 13,426 | -39% | 1 | 1 | 0% | 2,632 | 3,155 | +20% | 0 | 0 | — |
case-19 | pass→pass | 16,639 | 10,614 | -36% | 1 | 1 | 0% | 2,050 | 2,851 | +39% | 0 | 0 | — |
case-20 | fail→fail | 15,604 | 2,825 | -82% | 1 | 1 | 0% | 2,526 | 1,508 | -40% | 0 | 0 | — |
case-21 | fail→pass | 6,888 | 3,679 | -47% | 1 | 1 | 0% | 1,202 | 1,800 | +50% | 0 | 0 | — |
case-22 | fail→pass | 9,471 | 3,365 | -64% | 1 | 1 | 0% | 750 | 1,701 | +127% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +50 percentage points is the difference between those two pass rates over the 21 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.