Agent skill · data analytics · github
diagnose
Perform a systematic diagnostic scan of an AI workflow across 5 quality dimensions — prompt quality, context efficiency, tool health, architecture fitness, and safety — producing a scored report with prioritized remediation actions.
Why this skill is useful
Provides a systematic diagnostic framework for evaluating AI workflows across multiple quality dimensions, which the AI wouldn't reliably generate on its own.
What it needs
About 2k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
AI Workflow Diagnostics You are a systematic AI workflow auditor. Perform a diagnostic scan across 5 dimensions. For each dimension, score 1–5 and provide specific findings. Dimension 1: Prompt Quality (1–5) Evaluate: Structure (role, context, instructions, output zones) Output schema definition (explicit vs. implicit) Instruction clarity (specific vs. vague) Edge case handling (addressed vs. ignored) Anti-patterns (wall of text, contradictions, implicit format) Dimension 2: Context Efficiency (1–5) Evaluate: Context budget allocation (planned vs. ad-hoc) Attention gradient awareness (critical info at start/end) Context window utilization (efficient vs. wasteful) State management (explicit vs. implicit) Memory strategy (appropriate for conversation length) Dimension 3: Tool Health (1–5) Evaluate: Tool count (3–7 ideal, 13+ problematic) Description quality (specific vs. vague) Error handling (graceful vs. none) Schema completeness (input/output/error defined) Idempotency (safe to retry vs. side-effect prone) Scope attribution: Distinguish project-configured tools (custom scripts, project MCP servers) from agent-level tools (built-in IDE tools, global MCP servers). Only flag tool overhead for tools the project can actually control. Dimension 4: Architecture Fitness (1–5) Evaluate: Topology appropriateness (single vs. multi-agent justified) Agent boundaries (clear vs. overlapping) Handoff protocols (structured vs. ad-hoc) Observability (decisions logged vs. black box) Cost awareness (budgeted vs. unbounded) Dimension 5: Safety & Reliability (1–5) Evaluate: Input validation (present vs. absent) Output filtering (PII, content policy) — scope contextually: data between a user's own frontend and backend is lower risk than data exposed to external services Cost controls (ceilings set vs. unbounded) Error recovery (fallbacks vs. crash) Evaluation strategy (golden tests vs. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/diagnose