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BC apify-lead-scoring-enrichment

Score and enrich a CSV of B2B leads using Apify Actors. Takes a CSV with company URLs, free-text scoring rules, and an enrichment preference; runs BuiltWith (tech stack), Website Content Crawler (content classification), and Contact Info Scraper (company metadata) for scoring; enriches with either department-specific contacts (Contact Info Scraper + Bulk Email Finder fallback) or copywriter discovery (Google Search Scraper → AI Web Scraper → Bulk Email Finder). Outputs an enriched CSV with a numeric score and a per-lead outreach_hook column that personalizes cold email copy (e.g. "uses Shopify → send Shopify install guide"). Use when user asks to score leads, qualify leads, enrich a lead list, detect a company's tech stack for outreach, find marketing/sales/engineering contacts at a list of companies, hunt down blog copywriters for guest-post pitches, personalize cold email at scale, or turn a raw domain list into a ready-to-pitch account list.

apify/awesome-skills Agent Skills author: apify 13 files · 6 scripts body ≈ 4 096 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Score and enrich a CSV of B2B leads using Apify Actors.

As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

ProcedureShopifySales and CRMData and analyticstype 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
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
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: 12. 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 59/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 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
    • 70Execution cost. Instruction body is 4096 tokens
    • 100Steps. 35 steps
    • 100Failures and branches. 7 branches, has a failure section
    • 100Consistency. Name and required fields are in place

    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 958: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -31 of 6 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 35 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.