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BD energy-procurement

Procure electricity and natural gas for commercial and industrial facilities: tariff and rate-schedule optimization, demand-charge mitigation, supplier RFPs, fixed/index/block-and-index hedging, renewable PPA and REC evaluation, and sustainability reporting. Use when procuring energy, optimizing utility tariffs, managing demand charges, evaluating PPAs, or building energy budgets and hedge 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

Procure electricity and natural gas for commercial and industrial facilities: tariff and rate-schedule optimization, demand-charge mitigation, supplier RFPs…

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
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
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
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"

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 404: 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: 74.