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BC viral-outliers

Find viral outlier posts on TikTok, Instagram and YouTube, pull creator stats, transcribe and analyse videos, build competitor watchlists, and crawl profiles on demand. Prepaid credits, MCP or REST.

ClawHub Hermes author: MatsClaes2 v1.0.1 MIT-0 4 files body ≈ 2 349 tokens Open the sourceclawhub.ai↗ analyzed 2 h ago

Find viral outlier posts on TikTok, Instagram and YouTube, pull creator stats, transcribe and analyse videos, build competitor watchlists, and crawl profiles…

As a process C 51/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationYouTubeMarketingMedia and videoAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
82/100
safety, quality, tests
Safety 60%
90
Quality 40%
69
Run on models
none yet
Process rating
C
51/100
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

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.

Broad scope 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 skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • medium Exfiltration exfil-send-secrets-to-url references/skills.md:7
    Instruction to send secrets/history to an external endpoint (URL is a reference link, not a destination; describes API authentication (bearer / header / HTTPS); the skill's own vendor host)
    Authentication: send an API key as `Authorization: Bearer so_live_...` (or the `x-api-key` header). No key yet? Pay $15 for 1,500 credits at https://viraloutliers.com/api-access and get a key instantl
    reference linkAPI authvendor-host
  • medium Broad scope meta-requests-env-secret SKILL.md:1
    Skill asks the runtime to inject credential env vars into its sandbox: VIRAL_OUTLIERS_API_KEY — verify each one is needed for the stated purpose
    required_environment_variables: VIRAL_OUTLIERS_API_KEY

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 198 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • note frontmatter-key unknown frontmatter key "required_environment_variables"

Process rating: all ten parameters 51/100

  • 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. 16 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web, node) that frontmatter does not declare
  • 60Consistency. The Hermes dialect needs category and tags
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 40 steps
  • 100Execution cost. Instruction body is 2349 tokens

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 198: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 40 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
This looks like a legitimate social-media analytics skill, but it gives agents paid and account-changing actions and recommends unpinned npm execution without enough safeguards.
LLM: suspicious (high) · 13 Sept 2026