BD eval-harness
Eval-driven development (EDD) framework for AI coding sessions — define capability and regression evals before coding, grade with code-based, model-based, rule, or human graders, and track pass@k and pass^k reliability. Use when defining pass/fail criteria for agent tasks, measuring agent reliability, building regression suites for prompt or agent changes, or benchmarking across model versions.
Promises an eval-driven development framework: define pass/fail criteria upfront, grade with code, model, rule, or human graders, track pass@k and pass^k reliability. Single file with 2058 tokens of instructions. Inspection found broken references—some links in the document point nowhere. Quality score 77/100, process score 45/100 suggest the framework is sketched out but lacks detail. No critical issues flagged, but no model runs or sandbox tests either—untested on actual agents. Supports Claude, Cursor, DeepSeek, and many others.
Useful as a template if you need to structure agent test suites before deployment. Broken references mean you'll need to fix parts manually. Install if you're willing to patch it up.
Eval-driven development (EDD) framework for AI coding sessions — define capability and regression evals before coding, grade with code-based, model-based…
As a process D 45/100 · Unfinished process — References files that are not bundled: scripts/lib/eval-harness/
How to improve
- The text references files that are not there: add them or drop the references.
- 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: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: scripts/lib/eval-harness/ - note
frontmatter-keyunknown frontmatter key "tools"
Process rating: all ten parameters 45/100
- 0Tools and files. 1 referenced file(s) missing: scripts/lib/eval-harness/
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 50Failures and branches. 0 branches, has a failure section
- 100Steps. 43 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2058 tokens
- 100Progress reporting. Reports progress
- low 12 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (5 tags): a typed call is more reliable
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 397: enough signal without eating the budget
- +4Structure: 31 headings
- +3Step-by-step instructions: 43 items
- +4Has examples (14 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.