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AB cold-outreach-hunter

Meta-skill for orchestrating Apollo API, LinkedIn API, YC Cold Outreach, and MachFive Cold Email into a complete B2B cold outreach pipeline. Use when the user wants end-to-end lead sourcing, enrichment, personalized copy strategy, and generation-ready outreach sequences with strict quality and safety gates.

modbender/skill-library-mcp Claude Code author: modbender MIT 2 files body ≈ 2 413 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Meta-skill for orchestrating Apollo API, LinkedIn API, YC Cold Outreach, and MachFive Cold Email into a complete B2B cold outreach pipeline.

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, consistency, running it twice

IntegrationSales and CRMtype 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
B
68/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 68/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 9 mutating operations with no state check
    • 40Consistency. Frontmatter name (cold-outreach-hunter) differs from the folder (cold-outreach-skill)
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 110 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Failures and branches. 1 branches, has a failure section
    • 100Execution cost. Instruction body is 2413 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 10 top-level sections: this looks like several domains in one skill

    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)
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 308: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 110 items
    • +3Output format is stated explicitly
    • +4Has examples (6 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.