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BB Swelist — Tech Internship & Job Tracker

Helps you find tech internships and new-grad jobs, track every application locally, and prep for interviews — no sign-up needed

ClawHub Hermes author: Yuan Chen v1.0.13 MIT-0 2 files body ≈ 4 850 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 66/100 · Nearly there — weak spots: inputs and preconditions, consistency

ReferenceInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
B
66/100
Nearly there
Inputs and preconditions w 11
0
Consistency w 8
40
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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 name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-long-hermes description is 127 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "repository"
  • note frontmatter-key unknown frontmatter key "keywords"

Process rating: all ten parameters 66/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 40Consistency. Frontmatter name (Swelist — Tech Internship & Job Tracker) differs from the folder (swelist)
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 4850 tokens
  • 100Steps. 96 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 26 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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 127: enough signal without eating the budget
  • +4Structure: 42 headings
  • +3Step-by-step instructions: 96 items
  • +3Output format is stated explicitly
  • +4Has examples (26 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
The skill mostly matches its job-search purpose, but it asks agents to read a local profile file and handles resume/application data with unclear consent and persistence boundaries.
LLM: suspicious (high) · 10 Sept 2026