Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Use when the user wants to find problems, audit workflow quality, or get a comprehensive health check on their AI workflow.
.claude/skills/sharpdeveye-diagnose/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | 92% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 62% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 92% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 130% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 80% | 0% |
Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first.
Perform a systematic diagnostic scan across 5 dimensions. For each dimension, score 1-5 and provide specific findings.
Evaluate:
Evaluate:
Evaluate:
Evaluate:
Evaluate:
text╔══════════════════════════════════════╗ ║ MAESTRO DIAGNOSTIC ║ ╠══════════════════════════════════════╣ ║ Prompt Quality ████░ 4/5 ║ ║ Context Efficiency ███░░ 3/5 ║ ║ Tool Health ██░░░ 2/5 ║ ║ Architecture ████░ 4/5 ║ ║ Safety & Reliability ██░░░ 2/5 ║ ╠══════════════════════════════════════╣ ║ Overall Score: 15/25 ║ ╚══════════════════════════════════════╝ CRITICAL FINDINGS: 1. [Most severe issue — immediate action needed] 2. [Second most severe] 3. [Third] RECOMMENDED ACTIONS: 1. Run /fortify to add error handling (addresses Tool Health + Safety) 2. Run /streamline to reduce tool count (addresses Tool Health) 3. Run /refine for prompt structure improvements (addresses Prompt Quality)
Every recommended action MUST reference the specific Maestro command that addresses it. Use this mapping:
| Dimension Gap | Maestro Command | When to Recommend | |---------------|-----------------|-------------------| | Prompt structure, clarity, output schema | /refine | Score ≤ 4 on Prompt Quality | | Context budget, attention gradient, memory | /streamline | Score ≤ 3 on Context Efficiency | | Tool errors, missing tools, redundant tools | /fortify | Score ≤ 3 on Tool Health | | Tool count reduction, unused tools | /streamline | Tool count > 7 or unused tools found | | Safety gaps, error recovery, validation | /fortify | Score ≤ 3 on Safety & Reliability | | Test coverage, golden tests, evaluation | /guard | No automated tests or evaluation strategy | | Architecture boundaries, observability | /calibrate | Score ≤ 3 on Architecture Fitness |
Do NOT give generic manual actions (e.g., "Add Vitest", "Create a rollback script") without also specifying which Maestro command the user should run to implement it. The recommended action format is: > Run /<command> to specific action] (addresses Dimension] #gap number])
| Score | Meaning | Maestro Action | |-------|---------|----------------| | 5 | Production-excellent | No action needed | | 4 | Good with minor gaps | /refine for polish | | 3 | Functional but risky | /fortify or /streamline for targeted fix | | 2 | Significant issues | /fortify + /guard — immediate attention | | 1 | Broken or missing | /onboard-agent — rebuild required |
After diagnosis, run the command mapped to your lowest-scoring dimension. For a general improvement sequence: /fortify → /streamline → /refine.
NEVER:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 24,546 | 3,592 | -85% | 1 | 1 | 0% | 4,446 | 1,664 | -63% | 0 | 0 | — |
case-02 | fail→fail | 30,432 | 5,691 | -81% | 1 | 1 | 0% | 5,517 | 1,739 | -68% | 0 | 0 | — |
case-03 | fail→fail | 15,297 | 3,091 | -80% | 1 | 1 | 0% | 2,742 | 1,643 | -40% | 0 | 0 | — |
case-04 | pass→fail | 10,841 | 23,454 | +116% | 1 | 1 | 0% | 2,197 | 5,854 | +166% | 0 | 0 | — |
case-05 | pass→pass | 15,262 | 29,354 | +92% | 1 | 1 | 0% | 3,117 | 7,528 | +142% | 0 | 0 | — |
case-06 | pass→fail | 4,227 | 21,173 | +401% | 1 | 1 | 0% | 686 | 5,330 | +677% | 0 | 0 | — |
case-07 | fail→fail | 14,251 | 5,271 | -63% | 1 | 1 | 0% | 2,388 | 1,576 | -34% | 0 | 0 | — |
case-08 | fail→pass | 15,580 | 21,607 | +39% | 1 | 1 | 0% | 2,603 | 4,996 | +92% | 0 | 0 | — |
case-09 | fail→fail | 17,953 | 3,195 | -82% | 1 | 1 | 0% | 2,956 | 1,542 | -48% | 0 | 0 | — |
case-10 | fail→pass | 17,261 | 16,205 | -6% | 1 | 1 | 0% | 2,683 | 4,337 | +62% | 0 | 0 | — |
case-11 | fail→fail | 18,428 | 4,944 | -73% | 1 | 1 | 0% | 3,171 | 1,575 | -50% | 0 | 0 | — |
case-12 | fail→fail | 15,182 | 2,654 | -83% | 1 | 1 | 0% | 2,649 | 1,578 | -40% | 0 | 0 | — |
case-13 | pass→fail | 15,142 | 2,603 | -83% | 1 | 1 | 0% | 2,500 | 1,525 | -39% | 0 | 0 | — |
case-14 | fail→fail | 9,200 | 6,281 | -32% | 1 | 1 | 0% | 1,479 | 1,605 | +9% | 0 | 0 | — |
case-15 | fail→pass | 15,019 | 18,631 | +24% | 1 | 1 | 0% | 2,451 | 4,714 | +92% | 0 | 0 | — |
case-16 | fail→pass | 23,106 | 23,345 | +1% | 1 | 1 | 0% | 2,209 | 5,078 | +130% | 0 | 0 | — |
case-17 | fail→pass | 14,216 | 16,413 | +15% | 1 | 1 | 0% | 2,325 | 4,187 | +80% | 0 | 0 | — |
case-18 | fail→pass | 20,178 | 18,631 | -8% | 1 | 1 | 0% | 3,152 | 4,702 | +49% | 0 | 0 | — |
case-19 | fail→fail | 12,548 | 5,840 | -53% | 1 | 1 | 0% | 2,413 | 1,646 | -32% | 0 | 0 | — |
case-20 | fail→fail | 31,633 | 19,018 | -40% | 1 | 1 | 0% | 5,103 | 4,784 | -6% | 0 | 0 | — |
case-21 | fail→fail | 19,697 | 3,172 | -84% | 1 | 1 | 0% | 3,313 | 1,452 | -56% | 0 | 0 | — |
case-22 | fail→fail | 10,972 | 2,931 | -73% | 1 | 1 | 0% | 1,800 | 1,506 | -16% | 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 10 counted toward the lift figure. The other 12 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 +14 percentage points is the difference between those two pass rates over the 10 comparable cases. 4 cases got worse with the skill loaded, and they are included in that figure.
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.