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AB apify-reddit-scraper

Scrape Reddit with the harshmaur/reddit-scraper Apify Actor: keyword search across all of Reddit or inside one subreddit, full subreddit listings, post permalinks with complete comment threads, user profiles and community info, with date-range filters and optional delivery of results into Slack, Notion, Google Sheets or Airtable through Apify MCP connectors. Use when the user says "monitor my brand on Reddit", "find leads on Reddit", "what is Reddit saying about X", "scrape r/<subreddit>", "pull all comments from this Reddit thread", "Reddit posts about X since <date>", "track competitor mentions on Reddit", "Reddit sentiment for X" (labels are a paid add-on; otherwise quote the body), "build a Reddit dataset", "scrape a Reddit user's history", or any request for Reddit posts, comments, subreddits or users as structured data. No Reddit API key or login is needed. Out of scope: posting or replying on Reddit, private or quarantined communities, and semantic/topic search (Reddit search is literal).

apify/awesome-skills Agent Skills author: apify 3 files body ≈ 3 568 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Scrape Reddit with the harshmaur/reddit-scraper Apify Actor: keyword search across all of Reddit or inside one subreddit, full subreddit listings, post…

As a process B 73/100 · Nearly there — weak spots: result and completion, running it twice

ProcedureNotionSlackAirtableGoogle SheetsAI and agentstype 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
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
B
73/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
Tools and files w 18
60
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

    • note frontmatter-key unknown frontmatter key "author_url"

    Process rating: all ten parameters 73/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 16 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 29 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3568 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

    • +3Description length 1010: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 10 example trigger phrases
    • +4Description says when NOT to use the skill
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 29 items
    • +4Has examples (6 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.