ai-prompts-toolkit

name	ai-prompts-toolkit
description	GTM AI prompt library and prompt-loop patterns — Claygent research, LLM scoring, cold email drafts, reply classification, account briefs, and iterate-until-quality workflows for sales and marketing. Use when writing Clay AI prompts, designing prompt chains, or building research→draft→score loops in Clay, n8n, or Jesse. Triggers on: "GTM prompts", "Claygent prompt", "AI prompt loop", "cold email prompt", "reply classification prompt", "LLM column Clay", "prompt chain GTM", "research prompt sales".
license	MIT
compatibility	Claude Code, Jesse, Codex, Hermes, Windsurf, OpenCode, Gemini CLI, Copilot, Zed, VS Code, Goose
metadata	{"version":"1.0.0","author":"LeadMagic","category":"tools","tags":["ai-prompts","claygent","gtm","llm","cold-email","research","prompt-loops","automation"],"related_skills":["clay-toolkit","clay-loops-toolkit","clay-automation","cold-email-copywriting","meeting-prep","reply-handling","signal-scoring"],"frameworks":["Anthropic — Prompt Engineering for Tool Use","Clay — Claygent and AI column patterns","Winning by Design — SPICED discovery structure","Andy Whyte — MEDDICC evidence in prompts"]}

AI Prompts Toolkit

Overview

Generic AI prompts produce generic GTM output — invented personalization, pattern-guessed emails, and hallucinated metrics. GTM prompts need constraints: source URLs, word limits, banned claims, ICP context, and explicit failure behavior when data is missing.

This skill is the GTM prompt library: copy-paste prompts for Claygent, Clay AI columns, n8n LLM nodes, and Jesse agents — plus prompt loops that iterate research → draft → score → revise until quality gates pass.

When to Use

• "Write a Claygent prompt for [task]"
• "GTM prompt for cold email personalization"
• "Reply classification prompt"
• "Prompt loop for account research"
• "LLM column in Clay for ICP scoring"
• "AI prompt chain for outbound"

Load gtm-context first if ICP/positioning is undefined — prompts without context hallucinate.

Authoritative Foundations

• Anthropic — Prompt Engineering. Separate instructions from data; specify output format; define what to do when information is missing (return empty, not guess).
• Clay Claygent. Web research agent — must require source_url on every factual claim. Credit-heavy — use only after structured enrich fails.
• SPICED (WbD). Discovery and research prompts map to Situation, Pain, Impact, Critical Event, Decision — not free-form summaries.
• MEDDICC (Whyte). Research prompts for enterprise deals extract evidence for Metrics, Champion, Economic Buyer — score confidence 0/1/2.

Prompt Design Rules (All GTM Prompts)

1. Role + task — one sentence each
2. Input variables — name every field ({{company}}, {{domain}})
3. Output format — JSON or markdown template with required keys
4. Constraints — word limits, banned phrases, no invented stats
5. Missing data — "If unknown, return null — do not guess"
6. Source requirement — factual claims need source_url

GTM Prompt Catalog

Load full copy-paste prompts from references/prompt-library.md.

Prompt ID	Use Case	Tool
P01	Account snapshot (SPICED)	Claygent / LLM
P02	Work email find (no guess)	Claygent
P03	Signal line for cold email	LLM column
P04	Full cold email draft	LLM column
P05	Email quality score (1–10)	LLM column
P06	Reply classify (interested/objection/OOO)	LLM / n8n
P07	ICP fit score with reasoning	LLM column
P08	Meeting brief pre-call	Jesse / LLM
P09	Champion identification	Claygent
P10	Competitor mention extractor	Claygent

Prompt Loops (GTM)

Prompt loops chain multiple AI steps with gates between them.

Loop 1: Research → Brief (account prep)

Step 1 P01 Account snapshot →
Step 2 Gap check (missing Pain/CE?) →
Step 3 Targeted Claygent fill (only gaps) →
Step 4 Merge into meeting brief
Gate: Critical Event present OR flag manual review

Loop 2: Signal → Draft → Score → Revise (outbound)

Step 1 Enrichment + signal detect →
Step 2 P03 signal line (source required) →
Step 3 P04 email draft (<90 words) →
Step 4 P05 quality score →
Step 5 IF score <7: P04 revise with feedback (max 2 iterations) →
Step 6 IF score ≥7: route to human review queue
Gate: no send without human approval (pilot mode)

Loop 3: Enrich → ICP Score → Route

Step 1 Waterfall enrich →
Step 2 P07 ICP score →
Step 3 Route: ≥80 sequencer queue | 50–79 SDR review | <50 archive
Gate: suppression list check before route

Loop 4: Inbound Reply → Classify → Route

Step 1 P06 classify reply →
Step 2 Map to playbook (interested→AE, objection→`reply-handling`, OOO→pause) →
Step 3 CRM task + Slack alert
Gate: positive intent → human handoff within 1 business day

Use templates/prompt-loop-blueprint.md to document custom loops.

Example: P04 Cold Email Draft (abbreviated)

You write B2B cold emails for {{company_selling}}.

INPUT:
- Prospect: {{first_name}} {{last_name}}, {{title}} at {{company}}
- Signal: {{signal}} (source: {{source_url}})
- ICP pain: {{icp_pain}}
- Proof point: {{proof_point}} (must be factual)

RULES:
- Under 90 words
- One pain, one proof, one CTA
- No "I hope this finds you well", no invented metrics
- If signal or proof is empty, write generic ICP pain only — do not invent signal

OUTPUT JSON:
{"subject":"","body":"","personalization_source":"","word_count":0}

Full prompts in references/prompt-library.md.

Output Format

Deliverable: prompt spec or loop blueprint with prompt IDs, variable map, quality gates, iteration limits, credit budget (Claygent), and integration point (Clay column, n8n node, or agent skill).

Quality Check

• Every prompt has role, inputs, output format, constraints, missing-data rule
• Factual prompts require source_url in output
• Loops have explicit gates and max iteration counts
• Outbound loops include human review gate before send
• Prompts reference ICP/positioning from gtm-context — not generic SaaS
• Claygent prompts prohibit email pattern-guessing
• Reply loop maps to reply-handling categories

Common Pitfalls

1. "Find their email" Claygent prompts. 40–60% bounce from guessed patterns. Fix: require source URL; return empty if not found.

2. Unbounded revise loops. LLM iterates forever, burns credits. Fix: max 2 revisions; then human queue.

3. No quality scorer between steps. Bad drafts propagate. Fix: P05 score gate ≥7 before human review.

4. Prompt without suppression context. AI contacts opted-out accounts. Fix: pass suppressed: true/false; halt if true.

5. One prompt does everything. Research + draft + send in one call = errors. Fix: use prompt loops with narrow steps.

Execution Artifacts

• references/framework-notes.md — design rules and SPICED/MEDDICC mapping
• templates/output-template.md — Primary deliverable shell
• scripts/check-output.py — validates prompt specs and loop blueprints
• references/prompt-library.md — full GTM prompt catalog (P01–P10+)
• references/prompt-loop-patterns.md — loop diagrams and gate rules
• templates/prompt-spec.md — single prompt documentation template
• templates/prompt-loop-blueprint.md — multi-step loop template

Related Skills

• clay-toolkit — Where LLM columns and Claygent live in tables
• clay-loops-toolkit — Scheduled signal loops using these prompts
• cold-email-copywriting — Message strategy behind P03/P04
• meeting-prep — Consumes P01/P08 output
• reply-handling — Playbook for P06 routing
• ai-sdr-setup — Guardrails for automated prompt loops