SKILLEMALL.ai

DC data-flywheel

(no description)

codejunkie99/agentic-stack Agent Skills author: codejunkie99 1 file body ≈ 667 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Use this skill when a user wants to turn repeated human-approved agent work across Claude Code, Hermes, OpenClaw, Codex, Cursor, or custom .agent/ loops

As a process C 62/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
D
73/100
safety, quality, tests
Safety 60%
100
Quality 40%
33
Run on models
none yet
Process rating
C
62/100
Has gaps
When it triggers w 12
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Add a description to the frontmatter: without it the skill never triggers.
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

  • error description-missing SKILL.md: no `description` — the skill can never trigger
  • note frontmatter-key unknown frontmatter key "triggers"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "preconditions"
  • note frontmatter-key unknown frontmatter key "constraints"

Process rating: all ten parameters 62/100

  • 0When it triggers. No condition that starts the skill
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 3 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 34 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 667 tokens
  • low The response is described with custom markup (4 tags): a typed call is more reliable

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 0: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 34 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 33.