AF spec-driven-development
Design and run a Spec-Driven Development (SDD) pipeline for AI software factories — where structured specifications are the input, AI agents generate the code, and quality gates enforce correctness at each phase: SPECIFY → DECOMPOSE → IMPLEMENT → VERIFY → DELIVER. Use when building or refining a spec-driven pipeline any AI coding tool (Claude Code, Cursor, Hermes Agent, Devin, OpenHands, droid) can follow, or when you need spec quality gates, phase-gate verdicts, NFR encoding, or format translation. Do not use for a single small change with a clear goal (classify first via bmad), for the control-plane protocol of intent contracts, autonomy gating, and failure routing around a pipeline (bmad), or for unvalidated problems (product-discovery).
Design and run a Spec-Driven Development (SDD) pipeline for AI software factories — where structured specifications are the input, AI agents generate the…
As a process F 45/100 · Will not run — References files that are not bundled: ../product-design-and-ux/SKILL.md
How to improve
- The text references files that are not there: add them or drop the references.
- 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: 17. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: ../product-design-and-ux/SKILL.md
Process rating: all ten parameters 45/100
- 0Tools and files. 1 referenced file(s) missing: ../product-design-and-ux/SKILL.md
- 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
- 40Result and completion. Does not say what the result is
- 50When it triggers. No condition that starts the skill
- 100Steps. 17 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3547 tokens
- 100Running it twice. No mutating operations
- low 11 top-level sections: this looks like several domains in one skill
- 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
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 750: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 17 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (9 of 9)
- +3All 2 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.