SKILLEMALL.ai

BD GraphQL Schema Audit

GraphQL schema static auditor. Reads any .graphql SDL file or introspection JSON to detect N+1 exposure hotspots (nested list-within-list queries with no dataloader hint), unbounded query depth vulnerabilities (no max depth limit configured), deprecated fields still used in operations, naming convention violations (types not PascalCase, fields not camelCase, enums not UPPER_SNAKE_CASE), circular type references, missing pagination on collection fields, and overly broad scalars (String fields that should be typed as ID, Email, or URL). Outputs a prioritized issue list with resolver-level fix suggestions and a query complexity budget recommendation. Zero external API — pure local file analysis. Triggers on "graphql schema", "graphql audit", "schema review", "N+1 graphql", "query depth", "graphql lint", "/graphql-schema-audit".

ClawHub Agent Skills author: Lucius Pang v1.0.3 MIT-0 2 files body ≈ 5 728 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process D 43/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerInfrastructureSecuritytype and topics are labelled automatically from the skill text
JSON
Technical rating
B
83/100
safety, quality, tests
Safety 60%
100
Quality 40%
57
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5728 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 43/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 5 mutating operations with no state check
  • 40Consistency. Frontmatter name (GraphQL Schema Audit) differs from the folder (phy-graphql-schema-audit)
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 5728 tokens
  • 100Steps. 8 steps

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 836: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • +2Single-language instructions
  • +5Description quotes 7 example trigger phrases
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 8 items
  • +4Has examples (17 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.

External checks

ClawHub: clean
This is a local GraphQL schema review skill whose file reading is disclosed and fits its stated purpose.
LLM: benign (high) · 28 May 2026