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CD Rankscale GEO Analytics

Fetch and interpret Rankscale GEO (Generative Engine Optimization) analytics. Pulls brand visibility score, citation rate, sentiment, and top AI search terms.

ClawHub Agent Skills author: Rankscale AI v1.0.11 19 files body ≈ 2 420 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorData and analyticsMarketingInfrastructuretype 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
C
66/100
safety, quality, tests
Safety 60%
65
Quality 40%
68
Run on models
none yet
Process rating
D
41/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 7

✓ No critical or high findings

Medium and low: 7
  • medium Exfiltration net-credential-use API_ENDPOINT_CLARIFICATION.md:34
    Credential used in a network call (verify the destination is the intended service)
    curl -H "Authorization: Bearer $RANKSCALE_API_KEY" \
  • medium Exfiltration net-credential-use docs/API_ENDPOINT_CLARIFICATION.md:46
    Credential used in a network call (verify the destination is the intended service)
    curl -H "Authorization: Bearer $RANKSCALE_API_KEY" \
  • medium Exfiltration net-credential-use docs/API_ENDPOINT_CLARIFICATION.md:52
    Credential used in a network call (verify the destination is the intended service)
    curl -H "Authorization: Bearer $RANKSCALE_API_KEY" \
  • medium Exfiltration net-credential-use references/api-integration.md:351
    Credential used in a network call (verify the destination is the intended service)
    curl -H "Authorization: Bearer $API_KEY" \
  • medium Exfiltration net-credential-use references/api-integration.md:355
    Credential used in a network call (verify the destination is the intended service)
    curl -H "Authorization: Bearer $API_KEY" \
  • medium Exfiltration net-credential-use references/api-integration.md:359
    Credential used in a network call (verify the destination is the intended service)
    curl -H "Authorization: Bearer $API_KEY" \
  • medium Exfiltration net-credential-use references/api-integration.md:363
    Credential used in a network call (verify the destination is the intended service)
    curl -H "Authorization: Bearer $API_KEY" \

Files scanned: 19. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 41/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (Rankscale GEO Analytics) differs from the folder (rs-geo-analytics)
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 100Steps. 16 steps
  • 100Execution cost. Instruction body is 2420 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
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 158: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (14 code blocks)
  • +4Reference files are cited in the instructions (5 of 10)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: suspicious
This appears to be a legitimate Rankscale analytics skill, but it needs Review because broad triggers and API-key handling could expose account analytics or credentials unintentionally.
LLM: suspicious (high) · VirusTotal: benign · 28 May 2026