SKILLEMALL.ai

AB read-image

Generate textual descriptions of one or more images when the current session model (e.g. opencode/big-pickle) has no image input, by delegating to a vision-capable model on OpenRouter via `opencode run` with --file. Use when the user drops photos into imgs/, asks "can you read/describe these images", or an image needs a description for an article, portfolio, infographic, or video pipeline. Related terms: 看图, 描述图片, image description, vision model, glm-4.6v, --file.

ClawHub Agent Skills author: Jeff Yang v1.0.0 MIT-0 2 files body ≈ 1 068 tokens Open the sourceclawhub.ai analyzed 2 d ago

Generate textual descriptions of one or more images when the current session model (e.g. opencode/big-pickle) has no image input, by delegating to a…

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

GeneratorInfrastructureWriting and documentsMedia and videotype 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
Failures and branches w 10
0
Progress reporting w 2
0
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

    ✓ 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
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 10 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1068 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 468: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 10 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)

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

    External checks

    ClawHub: clean
    This skill does what it claims: it helps describe user-supplied images by sending selected image files to an OpenRouter vision model.
    LLM: benign (high) · VirusTotal: · 17 Aug 2026