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AB tech-trending

Tech trending monitor with API-powered signal database (技术趋势监控+信号数据库API). Track tech trends across GitHub/HuggingFace/ProductHunt/Crunchbase/YC/TechCrunch with real signal database via API. Features: (1) API-powered signal clusters covering AI Agent/AI Coding/Open Source AI/MCP/AI Video/GEO, (2) Market size data for each trend ($5.2B→$47B AI Agent, $3.2B→$27B AI Coding), (3) Three-layer filtering methodology (trend ≠ product validation ≠ domestic window), (4) Startup criteria: 3+ platform validation + overseas paid validation + domestic window 3-6 months, (5) Executable trending.sh script for CLI access. Use when: tech trends, market research, startup ideas, investment signals, trend analysis, opportunity identification. Triggers: tech trends, market research, startup ideas, investment signals, trend analysis, opportunity identification, trending tech, AI trends, tech signals, 技术趋势, 市场研究, 创业方向, 投资信号, 趋势分析, trending API, signal database.

ClawHub Agent Skills author: lm203688 v1.0.0 MIT-0 3 files · 1 script body ≈ 721 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 67/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, progress reporting

IntegrationGitHubAI and agentsSoftware developmenttype 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
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
67/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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: 3. 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 67/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 29 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 721 tokens
    • 100Running it twice. No mutating operations

    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)
    • +3Description length 950: 120–800 characters recommended
    • +1No license
    • +2Single-language instructions
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 29 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    The skill appears to be a trend-research helper whose API-backed script behavior fits its purpose, but users should know it may send research queries to an external service.
    LLM: benign (medium) · VirusTotal: · 28 May 2026