SKILLEMALL.ai

AB terminal-ops

Evidence-first repo execution workflow for ECC. Use when the user wants a command run, a repo checked, a CI failure debugged, or a narrow fix pushed with exact proof of what was executed and verified.

The skillemall take

Promises a workflow for running commands in repos with execution proof—for CI debugging, code checks, or verified pushes. Single instruction file at 683 tokens, no scripts. Static checks show grade A: safety 100, quality 87, process 71. No critical findings, linter clean. No model runs or sandbox tests—evaluated on structure alone.

Supports 10 platforms from Claude to DeepSeek. Process score of 71 hints at documentation gaps, but doesn't break anything. Install if you need a repeatable schema for traced repo operations—skill isn't broken or unsafe.

affaan-m/everything-claude-code Agent Skills author: affaan-m MIT 1 file body ≈ 683 tokens Open the sourcegithub.com↗ analyzed 21 h ago

Evidence-first repo execution workflow for ECC.

As a process B 71/100 · 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
71/100
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 71/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 8 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 41 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 683 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 200: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 41 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)

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