SKILLEMALL.ai

AB codex-token-usage

Summarize Codex token usage from local Codex Desktop or CLI session JSONL logs. Use when the user asks to count, audit, total, compare, or report Codex/OpenAI token usage for a period such as today, this week, last month, a calendar month, a rolling 30-day window, peak week, peak day, input/output/cached/reasoning breakdown, or net token usage.

ClawHub Agent Skills author: helenalhq v1.0.0 MIT-0 5 files body ≈ 909 tokens Open the sourceclawhub.ai analyzed 2 d ago

Summarize Codex token usage from local Codex Desktop or CLI session JSONL logs.

As a process B 67/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

IntegrationSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
B
67/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

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

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 67/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 17 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 909 tokens
    • 100Running it twice. No mutating operations

    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)
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • -31 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 346: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 17 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    External checks

    ClawHub: clean
    This skill appears benign: it reads local Codex session logs to calculate token usage and does not show network transfer, persistence, or hidden behavior.
    LLM: benign (high) · VirusTotal: · 9 Jun 2026