BC dedupe-against-corpus
Checks a candidate seed against the existing substacker corpus (seeds, drafts, published) for exact duplicates (sha256 fingerprint) and near-duplicates (title Jaccard, first-200-word Jaccard, shared topic cluster). Exact match exits as SKIPPED. Near-match links via related_seeds rather than creating a duplicate. Use after topic tagging and density scoring, before writing the seed. Trigger keywords — dedupe, duplicate, already thought, near-match, related seed, fingerprint.
Checks a candidate seed against the existing substacker corpus (seeds, drafts, published) for exact duplicates (sha256 fingerprint) and near-duplicates (title…
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
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: 1. 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 51/100
- 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. 9 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 743 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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 477: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.