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BB tiktok-search

Search TikTok videos, collect creator videos, retrieve detail data and comments for one known video, and run product, trend, competitor, and content insights through Gecho Bridge MCP. Requires the Gecho Chrome extension, an active TikTok session, and the Gecho Bridge MCP server.

ClawHub Hermes author: Gecho AI v1.1.37 MIT-0 3 files body ≈ 5 547 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process B 71/100 · Nearly there — weak spots: running it twice

ProcedureMarketingMedia and videoAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
59
Run on models
none yet
Process rating
B
71/100
Nearly there
Running it twice w 4
30
When it triggers w 12
50
Tools and files w 18
60
the three weakest of ten parameters · all ten

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.
  3. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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

  • warning description-long-hermes description is 279 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
  • warning body-long SKILL.md body ≈ 5547 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 71/100

  • 30Running it twice. 4 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 60Consistency. The Hermes dialect needs category and tags
  • 70Execution cost. Instruction body is 5547 tokens
  • 85Steps. 122 steps, 1 vague phrases
  • 100Inputs and preconditions. Inputs and preconditions are listed
  • 100Failures and branches. 22 branches, has a failure section
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 17 top-level sections: this looks like several domains in one skill

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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 279: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 122 items
  • +3Output format is stated explicitly
  • +4Has examples (11 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.

External checks

ClawHub: clean
This is a disclosed TikTok research skill that uses Gecho's MCP and Chrome extension to collect TikTok data and save results locally, with no artifact-backed hidden or destructive behavior.
LLM: benign (medium) · VirusTotal: · 21 Aug 2026