SKILLEMALL.ai

AB messages-ops

Evidence-first live messaging workflow for ECC. Use when the user wants to read texts or DMs, recover a recent one-time code, inspect a thread before replying, or prove which message source was actually checked.

The skillemall take

The skill promises a workflow for reading messages, recovering codes, and inspecting threads before replying. One instruction file with 655 tokens, no scripts or broken references. Quality scores 87/100, but process score sits at 68—the instruction works, yet its structure or step sequence has room for refinement. No critical findings, lint passes clean. Not run on models, not tested in sandbox.

Install it: the skill clears basic checks and runs on major platforms. However, the process score hints that you'll need to tune the instruction for complex scenarios yourself.

affaan-m/everything-claude-code Agent Skills author: affaan-m MIT 1 file body ≈ 655 tokens Open the sourcegithub.com↗ analyzed 21 h ago

Evidence-first live messaging workflow for ECC.

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

ProcedureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
68/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: RA-Skills, RA-Skills

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: 1. 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 68/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 39 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 655 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 211: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 39 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.