Ecosystem Standards Review Protocol

This skill details how to perform an audit on new or existing capabilities (Skills, Plugins, Workflows, Sub-Agents, and Hooks) against authoritative ecosystem specifications to ensure they are created, installed, and structured correctly.

Instructions

When invoked to review a codebase component or a planned extension:

Identify the Component Type: Determine if the subject is a Plugin boundary, an Agent Skill, an Antigravity Workflow/Rule, a Sub-Agent, or a Hook.
Recall the Specs: Before reviewing, read the relevant specification file found in the
```
ecosystem-authoritative-sources
```
skill library.
- Path:
```
../../references/*.md
```
Perform Rigorous Audit:
- Structure: Does the directory schema match the standard (
```
.claude-plugin/plugin.json
```
  ,
```
my-skill/SKILL.md
```
  )? Are all supporting files strictly organized into the official optional directories (
```
scripts/
```
  ,
```
references/
```
  ,
```
assets/
```
  ) rather than cluttering the skill root?
- Naming: Verify the skill name uses the gerund form (
```
verb + -ing
```
  , e.g.,
```
analyzing-spreadsheets
```
  ). Reject generic nouns. Ensure the
```
name
```
  is 1-64 lowercase alphanumeric chars/hyphens only, contains NO consecutive hyphens (
```
--
```
  ), and EXACTLY matches the parent directory name.
- Content: Does the YAML frontmatter adhere precisely to rules (
```
description
```
  1-1024 chars,
```
compatibility
```
  max 500 chars,
```
metadata
```
  strictly string-to-string keys/values)? Provide the recommendation to run
```
skills-ref validate ./my-skill
```
  to definitively catch parse errors.
- Description Viewpoint: Ensure the
```
description
```
  is written strictly in the third person ("Extracts text", not "I extract text") and isn't overly vague.
- Progressive Disclosure: For Skills, is the
```
SKILL.md
```
  file appropriately constrained (< 500 lines) with extraneous detail pushed to one-level deep reference files? Reject deeply nested reference chains.
- Reference Paths: Verify that all file references are strictly relative to the skill's root (e.g.,
```
scripts/extract.py
```
  ), avoiding absolute paths outside the plugin boundaries.
- Reference Readability: Do reference files >100 lines contain a Table of Contents for partial-read navigation?
- Script Quality: Verify python utility scripts do not punt errors back to the LLM (e.g., failing silently), but instead handle exceptions safely or emit clear
```
stderr
```
  messages. Ensure they don't use undocumented "magic numbers" (voodoo constants).
- Multi-CLI Support: When integrating agent CLI plugins, support exists for
```
claude-cli
```
  ,
```
gemini-cli
```
  , and
```
copilot-cli
```
  . Plugins must reflect the native CLI syntax in their system files.
- Anti-Patterns: Check for hardcoded credentials and Windows style paths (
```
\
```
  ).
- Connector Abstraction: If the plugin uses MCP tools, does it include a
```
CONNECTORS.md
```
  using the
```
~~category
```
  abstraction pattern instead of hardcoding specific tool names? This is required for portability. Ensure all native tool calls use the fully qualified
```
ServerName:tool_name
```
  format.
- Interaction Design Quality: For skills with user interaction, verify they use appropriate patterns:
  - Discovery phases use progressive questioning (broad → specific), not question walls
  - Decision points offer numbered option menus (3-7 items max)
  - Expensive operations have confirmation gates
  - Multi-step workflows include inline copyable checklists.
  - Skills end with next-action menus, not dead ends
  - Workflows taking long documents gracefully degrade using Document Format Agnosticism.
- Execution Safety (Plan-Validate-Execute): Do destructive or massive workflows mandate an intermediate verifiable plan file (e.g.,
```
changes.json
```
  ) before execution?
- Dual-Mode Architecture: If the skill both creates new artifacts AND improves existing ones, verify it implements the Bootstrap + Iteration dual-mode pattern with separate sections and trigger phrases.
- Output Templates: If the skill generates reports or artifacts, verify it either defines an output template or negotiates the format with the user.
- Escalation and Safety: Workflows with external risk must explicitly implement Graduated Autonomy Routing and Escalation Trigger Taxonomies rather than blanket-stopping on all issues.
- Source Transparency: Data synthesis output MUST conclude with explicit
```
Sources Checked
```
  and
```
Sources Unavailable
```
  blocks.
Produce Feedback: Provide explicit, granular feedback outlining exactly which ecosystem constraints were violated and concrete suggestions for fixing them. Ensure your feedback is actionable.

ecosystem-standards

NPX Install

Tags

SKILL.md Content

Ecosystem Standards Review Protocol

Instructions