AD mapbox-mcp-runtime-patterns
Integration patterns for Mapbox MCP Server in AI applications and agent frameworks. Covers runtime integration with pydantic-ai, mastra, LangChain, and custom agents. Use when building AI-powered applications that need geospatial capabilities.
As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 5
✓ No critical or high findings
Medium and low: 5
-
low Secrets in code
secret-high-entropy-tokenexamples/typescript/package-lock.json:348High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; test fixture / example file)"integrity": "sha5…lb4+9Suy…TCw/mYsk…csA==",
detectorfixture -
low Secrets in code
secret-high-entropy-tokenexamples/typescript/package-lock.json:457High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; test fixture / example file)"integrity": "sha5…GLi+2W/6ao+6Y7gu/RCwR…Kng==",
detectorfixture -
low Secrets in code
secret-high-entropy-tokenexamples/typescript/package-lock.json:492High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; test fixture / example file)"integrity": "sha5…F4j+6INO…yXw==",
detectorfixture -
low Secrets in code
secret-high-entropy-tokenexamples/typescript/package-lock.json:586High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; test fixture / example file)"integrity": "sha5…Jqj+nOOr…S5A==",
detectorfixture -
low Secrets in code
secret-high-entropy-tokenexamples/typescript/package-lock.json:788High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; test fixture / example file)"integrity": "sha5…xCA+ORZv…wO5/ywWF…Tag==",
detectorfixture
Files scanned: 21. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 64 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1397 tokens
- medium 4 test cases, all positive: not one "should refuse" or "should ask first"
- low No test case covers injection arriving through data
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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 243: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 64 items
- +4Has examples (5 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.