Skills

Skill 137 of 200

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…

2 minutes · 385 words · 9 sections

Install

$ npx skills add github/awesome-copilot --skill diagnose

$ npx skills add github/awesome-copilot

» /plugin marketplace add github/awesome-copilot

The first command installs just this skill, by the name in its SKILL.md; the second installs the whole repository.

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. “it seems to work”)

Diagnostic Report Format

╔══════════════════════════════════════╗
║          WORKFLOW 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. [Specific remediation for finding #1]
2. [Specific remediation for finding #2]
3. [Specific remediation for finding #3]

Scoring Guide

Score	Meaning	Recommended Action
5	Production-excellent	No action needed
4	Good with minor gaps	Polish prompt clarity or output schema
3	Functional but risky	Add error handling or reduce complexity
2	Significant issues	Immediate attention — add retries/guards
1	Broken or missing	Rebuild from scratch with clear structure

Usage

Invoke this skill when you want to:

Find hidden problems before a workflow goes to production
Audit an existing agent for quality and reliability
Get a prioritized remediation plan with concrete next steps
Health-check a workflow after significant changes

Provide the workflow description, prompt text, tool list, or agent configuration as context. The more detail you provide, the more precise the findings.

About this skill

Trigger

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.

The verbatim description from this skill’s front matter — the string an agent matches on to decide whether to load it.

Colophon for this issue

Colophon

How this issue was made

Source

github.com/github/awesome-copilot, branch main, last pushed 24 September 2026.

Licence

MIT — the text of every skill is reproduced unmodified, frontmatter included, under the upstream licence.

Discovery

200 skills found by walking the repository tree for SKILL.md, not by matching a directory convention. 2 distinct layouts observed: .github/skills/*/SKILL.md, skills/*/SKILL.md.

Authorship

3 of 200 skills are installed into the repository rather than published from it. They are shelved in the closing “Credited skills” part and remain the work of their own authors. A skill counts as credited when its only copy lives under one agent’s dot directory; visible directories, per-agent mirror sets, and skills installable from this repository on skills.sh all count as published.

Issue colours

Resolved from a curated brand profile — hue 295°, chroma 0.190. Two accent tones are generated per issue and each is proven against its own ground before it ships: a single accent that passes AA on both light and dark paper is arithmetically impossible.

Heading repairs

1 repair applied to this skill so the document has one h1 and no skipped levels:

Shifted “9 headings” from h1 to h2 so the skill title is the only h1.

Spec compliance

Every skill satisfies the Agent Skills specification’s frontmatter rules.

Images

Images inside a skill come from the upstream repository. Where the author gave no alternative text we mark the image decorative rather than inventing a description — a plausible caption we made up is worse than none for the reader who depends on it.

Marketplace

A plugin manifest is published at .github/plugin/marketplace.json by GitHub, declaring 162 plugins. It is read for editorial metadata only — never as the skill index, which is always the repository tree.

Signal

Install counts come from skills.sh. They measure downloads, not quality, and an unranked repository is not an unread one.

Agent surfaces

The whole issue is available as one markdown document at /github/awesome-copilot.md, and each skill at its own .md URL.

Publication

Set by Skills Docs from the source repository. Body text is Literata at the reader’s chosen size and measure; code is Geist Mono. Nothing on this page was written by us except this paragraph.

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. “it seems to work”)

Diagnostic Report Format

╔══════════════════════════════════════╗
║          WORKFLOW 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. [Specific remediation for finding #1]
2. [Specific remediation for finding #2]
3. [Specific remediation for finding #3]

Scoring Guide

Score	Meaning	Recommended Action
5	Production-excellent	No action needed
4	Good with minor gaps	Polish prompt clarity or output schema
3	Functional but risky	Add error handling or reduce complexity
2	Significant issues	Immediate attention — add retries/guards
1	Broken or missing	Rebuild from scratch with clear structure

Usage

Invoke this skill when you want to:

Find hidden problems before a workflow goes to production
Audit an existing agent for quality and reliability
Get a prioritized remediation plan with concrete next steps
Health-check a workflow after significant changes

Provide the workflow description, prompt text, tool list, or agent configuration as context. The more detail you provide, the more precise the findings.