SKILLEMALL.ai

AC terminal-screenshot

Render a terminal CLI program's colored output to a PNG so Claude can actually SEE the real visual result — color contrast, alignment, background blocks, highlighting — instead of only reading plain text and raw ANSI escape codes. Use this whenever verifying or debugging how a CLI tool looks in the terminal: delta git diff colors, bat syntax highlighting, starship prompt, eza/ls colors, git diff, ripgrep matches, or any ANSI-colored output. ALWAYS use it right after changing any CLI color config (delta / bat / themes / lazygit pager) to visually confirm the result rather than guessing from hex values — reading a hex code is not the same as seeing the rendered contrast on the real terminal background. Trigger phrases: 看终端效果, 终端截图, 验证配色, 配色对比, 终端真实效果, terminal screenshot, render terminal output, ANSI to image, "does this color look right", "is the contrast enough", delta/bat color verification.

daymade/claude-code-skills Agent Skills author: daymade 3 files · 1 script body ≈ 1 523 tokens Open the sourcegithub.com analyzed 2 h ago

Render a terminal CLI program's colored output to a PNG so Claude can actually SEE the real visual result — color contrast, alignment, background blocks…

As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationGitHubSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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: 3. 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 58/100

    • 0Result and completion. Does not say what the result is
    • 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
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 7 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1523 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (4 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 905: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 7 items
    • +4Has examples (4 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.