SKILLEMALL.ai

CB image-blast-uncover

Main image analysis skill, object generation, plate decisionmaking, and initial scene description. Use this skill when user wants to create objects and necessary for main image analysis.

neilsonnn/image-blaster Claude Code author: neilsonnn MIT 2 files body ≈ 1 212 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Main image analysis skill, object generation, plate decisionmaking, and initial scene description.

As a process B 66/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, running it twice

AnalyzerAI and agentsData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
B
66/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Inputs and preconditions w 11
30
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Unexpected flow-seq-start at node end at line 3, column 29: argument-hint: [world-name] [optional image paths or instructions] ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value

    Process rating: all ten parameters 66/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 9 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 38 steps, 1 vague phrases
    • 100Tools and files. Tools declared in frontmatter
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1212 tokens
    • low The response is described with custom markup (6 tags): a typed call is more reliable

    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)
    • +4Structure: 2 headings, hard to scan
    • +1No license
    • +2Single-language instructions
    • +3Description length 186: enough signal without eating the budget
    • +3Step-by-step instructions: 38 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.