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AF token-cost

Measure before optimizing — estimate token counts locally with stated heuristics, price them at your model's rates, and quantify before/after savings, because token optimization without measurement is vibes. Use when asked how many tokens is this, what does this context cost per call, is this optimization worth it, or compare these two versions' cost. Produces the estimate with both heuristics shown, the cost math at your prices across your call volume, and the before/after comparison that decides whether an optimization earned its complexity.

mohitagw15856/pm-claude-skills Agent Skills author: mohitagw15856 MIT 2 files body ≈ 1 152 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Measure before optimizing — estimate token counts locally with stated heuristics, price them at your model's rates, and quantify before/after savings, because…

As a process F 49/100 · Will not run — References files that are not bundled: ../token-diet/SKILL.md

GeneratorAI and agentsSoftware developmentData and analyticstype 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
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
F
49/100
Will not run
References files that are not bundled: ../token-diet/SKILL.md
Tools and files w 18
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: pm-claude-skills, pm-claude-skills

How to improve

  1. The text references files that are not there: add them or drop the references.
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 missing-ref reference to a missing file: ../token-diet/SKILL.md

Process rating: all ten parameters 49/100

Will not run. References files that are not bundled: ../token-diet/SKILL.md
  • 0Tools and files. 1 referenced file(s) missing: ../token-diet/SKILL.md
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 22 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1152 tokens

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 549: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 22 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.