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AC work-productivity-nano-banana-workflow-helper

Build practical AI image generation and editing workflows inspired by Nano Banana Pro demand, including prompt packs, reference planning, retry rules, and visual QA. Use when a user asks for Nano Banana, AI image generation, image editing, prompt pack, reference image, or needs practical workflow, code, checklist, documentation, or review support for this job.

ClawHub Agent Skills author: Kyro v0.1.0 MIT-0 7 files body ≈ 607 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Build practical AI image generation and editing workflows inspired by Nano Banana Pro demand, including prompt packs, reference planning, retry rules, and…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureWriting and documentsAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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: 7. 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 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 4 mutating operations with no state check
    • 40Consistency. Frontmatter name (work-productivity-nano-banana-workflow-helper) differs from the folder (work-productivity-nano-banana-workflow-helper-040526)
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. No external tools needed
    • 100Steps. 17 steps
    • 100Execution cost. Instruction body is 607 tokens
    • 100Progress reporting. Reports progress

    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)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 362: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 17 items
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This is a documentation-only helper for planning AI image workflows, with broad trigger wording but no executable code or sensitive access.
    LLM: benign (high) · VirusTotal: · 13 Jul 2026