SKILLEMALL.ai

AB context-window-tracker

Track and report OpenClaw context window usage with a detailed breakdown of what's consuming tokens. Use when: user asks about context usage, token usage, "how much context am I using", "how full is my context window", "tokens remaining", "am I close to the limit", thinking/reasoning token costs, what's eating context (session setup vs conversation vs overhead), or how many turns are left. NOT for: estimating tokens for arbitrary text, managing context (compact/prune), or cross-session cost aggregation.

ClawHub Agent Skills author: 99rebels v1.4.0 MIT-0 5 files body ≈ 1 462 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 78/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

ProcedureData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
100/100
safety, quality, tests
Safety 60%
100
Quality 40%
100
Run on models
none yet
Process rating
B
78/100
Nearly there
Progress reporting w 2
0
Inputs and preconditions w 11
30
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

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 78/100

    • 0Progress reporting. Says nothing while it works
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 31 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 5 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1462 tokens
    • 100Running it twice. Mutating operations check current state
    • high The skill tells the model to perform an irreversible action with no human approval

    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

    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 508: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 31 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 1 scripts are documented

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

    External checks

    ClawHub: clean
    The artifacts show disclosed ClawHub and Convex maintenance workflows, including some powerful staff and local-review commands, but no hidden exfiltration, destructive automation, or purpose-mismatched behavior.
    LLM: benign (medium) · VirusTotal: · 29 May 2026