SKILLEMALL.ai

BC capacity-workload-planner

Weekly capacity and workload register: available and allocated hours, utilisation percentage, over-allocation check and leave days. Use for resource planning.

The skillemall take

Promises a weekly workload register: available and allocated hours, utilisation percentage, over-allocation alerts, leave tracking. Resource planning on paper.

One file, no lint errors, no critical findings. Quality scored 79, process 66. Never run on models, sandbox untested. Works on Claude and Hermes. No blocks, links intact.

Set it up if you need a basic register without frills. You'll validate it against your actual data yourself.

sickn33/agentic-awesome-skills Hermes author: sickn33 MIT 1 file body ≈ 4 055 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Weekly capacity and workload register: available and allocated hours, utilisation percentage, over-allocation check and leave days.

As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureNotionExcelOperations and projectsData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: agentic-awesome-skills, agentic-awesome-skills

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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 158 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • note frontmatter-key unknown frontmatter key "risk"
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "source_type"
  • note frontmatter-key unknown frontmatter key "date_added"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "source_repo"

Process rating: all ten parameters 64/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 3 mutating operations with no state check
  • 60Failures and branches. 2 branches
  • 70Execution cost. Instruction body is 4055 tokens
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 39 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 13 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
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 158: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 39 items
  • +4Has examples (10 code blocks)

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