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AC golden-hour

Automates the "Golden Hour" Instagram engagement hack. Use this skill when a user wants to set up a bot that watches an Instagram Reel/Post for a specific keyword in the comments (like "BOT" or "GOLDEN") and automatically replies to the comment and sends a DM with a link or message.

ClawHub Agent Skills author: yadavabhijeet4 v1.0.0 MIT-0 3 files body ≈ 445 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ProcedureMarketingInfrastructuretype 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
96/100
safety, quality, tests
Safety 60%
99
Quality 40%
92
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Dangerous commands cmd-eval-dynamic scripts/ig_golden_hour.py:54
      Dynamic code execution from decoded/untrusted input (security demo / example)
      os.system(f"openclaw message send --target webchat --message '🚨 SUCCESS! I just sent the Mock Trading DM to an Instagram user who commented \"BOT\".'")
      demo

    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 54/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 4 mutating operations with no state check
    • 40Consistency. Frontmatter name (golden-hour) differs from the folder (golden-rule)
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 11 steps
    • 100Execution cost. Instruction body is 445 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

    • +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
    • +5Description quotes 2 example trigger phrases
    • +3Description length 283: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 11 items
    • +4Has examples (1 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: suspicious
    This skill openly automates Instagram replies and DMs, but it also runs a secondary OpenClaw notification command and lacks safeguards for account-impacting automation.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026