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BB email-lead-gen

OpenClaw Email Lead Generation — the complete outreach and pipeline system for your agent. Guided setup builds your config, Template Forge creates custom email sequences from a voice interview, and 3-tier architecture scales from manual pipeline tracking to fully automated cron-driven outreach. Add leads, score them, run email sequences, monitor replies, get morning briefings, and track your entire funnel — all through conversation. v1.0.1: timezone support, per-domain rate limits, email warmup, compliance/unsubscribe, audit logging, inbound HTML stripping, credential security, temp-file email body (fixes JSON escaping). Works standalone or alongside AI Persona OS. Built by Jeff J Hunter.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files · 1 script body ≈ 12 123 tokens Open the sourcegithub.com↗ analyzed 3 d ago

OpenClaw Email Lead Generation — the complete outreach and pipeline system for your agent.

As a process B 67/100 · Nearly there — weak spots: when it triggers, consistency, execution cost

ProcedureGmailSales and CRMInfrastructureAI and agentstype 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
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
B
67/100
Nearly there
When it triggers w 12
20
Consistency w 8
40
Execution cost w 6
40
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. 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 description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 12123 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 67/100

  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (email-lead-gen) differs from the folder (openclaw-email-lead-generation)
  • 40Execution cost. Instruction body is 12123 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 85Steps. 99 steps, 3 vague phrases
  • 100Inputs and preconditions. Inputs and preconditions are listed
  • 100Failures and branches. 5 branches, has a failure section
  • 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 29 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)
  • -225 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 697: enough signal without eating the budget
  • +4Structure: 45 headings
  • +3Step-by-step instructions: 99 items
  • +3Output format is stated explicitly
  • +4Has examples (23 code blocks)
  • +4Reference files are cited in the instructions (1 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.