AD Pipeline Contract Enforcer
Data pipeline contract enforcer. Define the expected schema at each pipeline stage boundary — field names, types, nullability, value ranges, business invariants — and validate actual data samples against those contracts. Catches schema drift between pipeline stages before it reaches production. Supports dbt models, Spark DataFrames, Pandas DataFrames, Kafka topics, REST API payloads, and raw CSV/JSON files. Can auto-generate a contract from a sample, validate a sample against an existing contract, detect when a contract has been broken by upstream changes, and produce a migration plan to fix violations. Zero external API — pure local file and CLI analysis. Triggers on "pipeline contract", "schema drift", "validate pipeline output", "data contract", "pipeline schema mismatch", "contract enforcement", "/pipeline-contract".
As a process D 43/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- 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-formatname should be kebab-case (lowercase letters, digits, hyphens) - note
frontmatter-keyunknown 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. 6 mutating operations with no state check
- 40Consistency. Frontmatter name (Pipeline Contract Enforcer) differs from the folder (phy-pipeline-contract-enforcer)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 4486 tokens
- 100Steps. 14 steps
- low 13 top-level sections: this looks like several domains in one skill
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 832: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +5Description quotes 7 example trigger phrases
- +4Structure: 18 headings
- +3Step-by-step instructions: 14 items
- +4Has examples (15 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.