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CF career-ops-plugin-gmail

How to pull job leads from a Gmail label into the career-ops pipeline.

The skillemall take

The skill promises to extract job leads from a Gmail label into your career pipeline. In reality: four files with two scripts that haven't run through any models or sandbox yet. Content scores 67, process scores 27—instructions exist, but how it actually works remains opaque. No critical errors, but no validation either.

Might work if Gmail is your main lead source. Install it if you're willing to test on live data. Behavior on other platforms is a mystery.

career-ops-hq/career-ops Agent Skills author: career-ops-hq MIT 4 files · 2 scripts body ≈ 205 tokens Open the sourcegithub.com↗ analyzed 5 d ago

How to pull job leads from a Gmail label into the career-ops pipeline.

As a process F 27/100 · No process to follow — weak spots: steps, result and completion, when it triggers

ProcedureGmailInfrastructuretype 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
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
F
27/100
No process to follow
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: career-ops-plugin-gmail (santifer/career-ops)

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: 4. 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 27/100

  • 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
  • 25Steps. 1 steps
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (career-ops-plugin-gmail) differs from the folder (gmail)
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 100Execution cost. Instruction body is 205 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
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 70: 120–800 characters recommended
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Structure: 4 headings
  • +4Has examples (1 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.