BC auto-apply
Automate your job search and application process with Mokaru. Search thousands of jobs, tailor your resume to each role, track applications through your pipeline, and get AI-powered career coaching. Supports remote and on-site roles across all industries. Use when users ask about job hunting, career search, applying for jobs, resume optimization, interview prep, or application tracking.
As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, execution cost
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 14542 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "requires"
Process rating: all ten parameters 58/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 57 mutating operations with no state check
- 40Execution cost. Instruction body is 14542 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 39 steps
- 100Failures and branches. 10 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- 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 389: enough signal without eating the budget
- +4Structure: 48 headings
- +3Step-by-step instructions: 39 items
- +4Has examples (69 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.