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AB monitoring-instagram-hashtag-trends

Monitors Instagram hashtag performance and trends using apidojo's Instagram scraper on Apify. Triggers when the user asks to: track Instagram hashtag performance, monitor trending hashtags in a niche on Instagram, find the best hashtags for a content category, analyze hashtag reach and engagement on Instagram, discover new hashtags gaining traction in an industry, compare hashtag performance for a brand, or build an optimal Instagram hashtag strategy for a post. Returns hashtag volume, avg engagement per post, growth trend, and top-performing posts per tag. Ideal for Instagram content creators, social media managers, and brand content teams.

ClawHub Agent Skills author: API Dojo v1.0.0 MIT-0 2 files body ≈ 1 048 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Monitors Instagram hashtag performance and trends using apidojo's Instagram scraper on Apify.

As a process B 78/100 · Nearly there — weak spots: running it twice, progress reporting

AnalyzerMarketingtype 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
94/100
safety, quality, tests
Safety 60%
98
Quality 40%
88
Run on models
none yet
Process rating
B
78/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Failures and branches w 10
50
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Exfiltration exfil-secret-in-url SKILL.md:83
      Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)
      curl -X POST   "https://api.apify.com/v2/acts/apidojo~instagram-scraper/runs?token=…"   -H "Content-Type: application/json"   -d '{"keywords": ["#[hashtag1]", "#[hashtag2]"], "maxItems": 50
      placeholder
    • low Exfiltration net-credential-use SKILL.md:83
      Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)
      curl -X POST   "https://api.apify.com/v2/acts/apidojo~instagram-scraper/runs?token=…"   -H "Content-Type: application/json"   -d '{"keywords": ["#[hashtag1]", "#[hashtag2]"], "maxItems": 50
      vendor-host

    Files scanned: 2. 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 78/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 8 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1048 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)
    • +2Single-language instructions
    • +3Description length 649: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 8 items
    • +3Output format is stated explicitly
    • +4Has examples (6 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    The skill is a disclosed Instagram hashtag analysis helper, but it needs review because it handles an Apify API token unsafely in a URL and documents broader scraping targets than its hashtag-focused purpose.
    LLM: suspicious (medium) · VirusTotal: · 3 Sept 2026