AB job-hunt-tracker
When user asks to track job applications, manage job search, log interview, applied for job, job application status, track where I applied, job search organizer, application follow up, offer comparison, job pipeline, application stats, track rejection, track offer, job board, career tracker, job search progress, or any job hunting task. 20-feature AI job hunt tracker with application pipeline, interview scheduler, offer comparator, follow-up reminders, rejection analyzer, and job search gamification. Replaces $40/month job tracking tools for free. All data stays local — NO external API calls, NO network requests, NO data sent to any server.
As a process B 66/100 · Nearly there — 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: 2. 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 66/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 4 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 70Execution cost. Instruction body is 4649 tokens
- 100Tools and files. No external tools needed
- 100Steps. 43 steps
- 100Failures and branches. 1 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
- low 29 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
- +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 648: enough signal without eating the budget
- +4Structure: 31 headings
- +3Step-by-step instructions: 43 items
- +4Has examples (45 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.