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BB seo-new-keywords-research

Turn new AI names or unstructured AI market signals into a keyword priority table. For each name it finds the generic search terms downstream of it, screens them for relevance, and checks novelty, demand size and momentum against Google Ads volume and DataForSEO Trends (an estimate of Google search interest), ending in prioritize, watch or skip. Use when the user has new model, product or repository names (or an AI listening report, a batch of posts, a changelog) and asks which keywords to build pages or tools for. Runs the new-term-radar command line; needs an AISA_API_KEY, and a GOANYAPI_API_KEY improves it.

zhuyansen/new-term-radar Agent Skills author: zhuyansen 1 file body ≈ 2 095 tokens Open the sourcegithub.com↗ analyzed 7 h ago

Turn new AI names or unstructured AI market signals into a keyword priority table.

As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorMarketingSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
30
Running it twice w 4
30
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: 1. 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 65/100

    • 0Result and completion. Does not say what the result is
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 6 mutating operations with no state check
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 70Failures and branches. 5 branches
    • 100Steps. 22 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2095 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 617: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 22 items
    • +4Has examples (1 code blocks)

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