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AB cold-email

Write B2B cold emails and follow-up sequences that earn replies. Use when creating outbound prospecting emails, SDR outreach, personalized opening lines, subject lines, CTAs, and multi-touch follow-up sequences.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 7 files body ≈ 1 768 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Write B2B cold emails and follow-up sequences that earn replies.

As a process B 70/100 · Nearly there — weak spots: result and completion, failures and branches, running it twice

GeneratorSales and CRMtype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
B
70/100
Nearly there
Failures and branches w 10
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: agentic-awesome-skills, agentic-awesome-skills

How to improve

    For the model run — optional
    • 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: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "risk"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "date_added"

    Process rating: all ten parameters 70/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 3 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 51 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1768 tokens
    • low 13 top-level sections: this looks like several domains in one skill
    • low No test case covers injection arriving through data

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 211: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 51 items
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.