SKILLEMALL.ai

BC apify-blocked-scrape-triage

Diagnose and recover a scrape that is blocked, throttled, or served empty or partial content, in cost order, before paying for a heavier Actor. Use when a run returns 0 items, when a site answers 403, 429 or a challenge page, when the HTML comes back empty or truncated, when a request fails with no response at all, when the page shows data in a browser that the Actor does not see, or when a scraper that worked yesterday suddenly returns nothing. Triggers - "I'm getting blocked", "403 from the site", "429 rate limited", "Cloudflare challenge", "scraper returns empty results", "connection reset", "page loads in my browser but not in the Actor", "do I need residential proxies", "should I switch to a browser", "my scraper broke overnight". Escalates in cost order - a cheaper published source first, then reachability, request rate and IP spread, request fidelity, session tokens, the site's own internal API, browser last.

apify/awesome-skills Agent Skills author: apify 5 files body ≈ 11 764 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Diagnose and recover a scrape that is blocked, throttled, or served empty or partial content, in cost order, before paying for a heavier Actor.

As a process C 62/100 · Has gaps — weak spots: result and completion, when it triggers, execution cost

AnalyzerCloudflareSoftware developmentData and analyticsAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Execution cost w 6
40
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 11764 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "author_url"

Process rating: all ten parameters 62/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 40Execution cost. Instruction body is 11764 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 51 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (3 tags): a typed call is more reliable

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)
  • +3Description length 929: 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
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 51 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.