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BC atlas-banana-imagetoimage

Edit or combine images using the AtlasCloud Nanobanana Edit model. Use when the user wants to modify an image based on another, such as swapping clothes, merging styles, or applying visual elements from one image to another. Triggers on phrases like: "Changing clothes in the photo", "swap clothes", "edit image with reference", "combine images", "apply the style of one image to another".

Not recommendedcritical or high security findings
ClawHub Agent Skills author: Guilherme Funchal da Silva v1.0.1 MIT-0 8 files body ≈ 861 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

GeneratorInfrastructuretype 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
B
78/100
safety, quality, tests
Safety 60%
77
Quality 40%
79
Run on models
none yet
Process rating
C
61/100
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

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Secrets in code
If you install

The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.

For the author

The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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 · 6

  • high Secrets in code meta-credential-files .env
    Credential / dotenv files bundled with the skill (1)
    .env
Medium and low: 5
  • low Secrets in code secret-high-entropy-token package-lock.json:21
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…9dM/mwVgvbZJaSNaRk+bshk…Kbz+IoId…W0Q==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:51
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…SDq+2kAA…MOe/+5cdoEdg==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:86
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…FrF+LTRo…W3g==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:95
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…A6j+hAmM…GbS+kf5c…csw==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:104
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…5bm+c2gQ…aG5+esrLODihIorn+Pe6F…dXA==",
    detector

Files scanned: 7. 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: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Edit or combine images using the AtlasCloud Nanobanana Edit model.… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value

Process rating: all ten parameters 61/100

  • 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
  • 40Consistency. Frontmatter name (atlas-banana-imagetoimage) differs from the folder (nano-banana-atlas-ai)
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 12 steps
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 1 branches, has a failure section
  • 100Execution cost. Instruction body is 861 tokens
  • 100Running it twice. Mutating operations check current state

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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 389: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 12 items
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: suspicious
This image-editing skill does what it claims, but it handles API credentials and generated image data too loosely for automatic trust.
LLM: suspicious (high) · VirusTotal: · 29 May 2026