SKILLEMALL.ai

CD energy-procurement

電気とガス調達、料金最適化、需要料金管理、再生可能エネルギーPPA評価、およびマルチファシリティーエネルギー戦略のための符号化された専門知識。 Codified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis, hedging strategies, load profiling, and sustainability reporting frameworks. Use when procuring energy, optimizing tariffs, managing demand charges, evaluating PPAs, or developing energy strategies.

affaan-m/everything-claude-code Agent Skills author: affaan-m MIT 1 file body ≈ 7 289 tokens Open the sourcegithub.com↗ analyzed 25 h ago

電気とガス調達、料金最適化、需要料金管理、再生可能エネルギーPPA評価、およびマルチファシリティーエネルギー戦略のための符号化された専門知識。 Codified expertise for electricity and gas procurement, tariff optimization, demand…

As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerProcurementtype and topics are labelled automatically from the skill text
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
D
48/100
Unfinished process
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 1 more place: ECC

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 body-long SKILL.md body ≈ 7289 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "origin"

Process rating: all ten parameters 48/100

  • 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. 43 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 70Execution cost. Instruction body is 7289 tokens
  • 85Steps. 77 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • low 11 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)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 590: enough signal without eating the budget
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 77 items
  • +4Has examples (0 code blocks)
  • +1License stated

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