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BC job-application-optimizer

Strategic job application planning and Resume SEO optimization. Approaches applications like marketing campaigns with market research, opportunity qualification, and content optimization. Activate on 'optimize resume', 'tailor resume', 'ATS optimization', 'job fit score', 'should I apply'. NOT for initial career narratives (career-biographer), portfolio design (cv-creator), or market positioning (competitive-cartographer).

curiositech/some_claude_skills Agent Skills author: curiositech 2 files body ≈ 1 662 tokens Open the sourcegithub.com↗ analyzed 39 min ago

Strategic job application planning and Resume SEO optimization.

As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

ProcedureAWSKubernetesPostgreSQLMarketingtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 63/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 105 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1662 tokens

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
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 426: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 105 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.