v1.0.0 to v1.0.0

---

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

"AI prompt loop", "cold email prompt", "reply classification prompt", "LLM

column Clay", "prompt chain GTM", "research prompt sales".

license: MIT

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

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 |

| `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/prompt-library.md` — full GTM prompt catalog (P01–P10+)

- `references/prompt-loop-patterns.md` — loop diagrams and gate rules

- `references/framework-notes.md` — design rules and SPICED/MEDDICC mapping

- `templates/prompt-spec.md` — single prompt documentation template

- `templates/prompt-loop-blueprint.md` — multi-step loop template

- `scripts/check-output.py` — validates prompt specs and loop blueprints

## 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