create-proposal: Reusable for AI Agents | OpenAgentSkill

Overview

Generate PandaDoc proposals from client information or sales call transcripts. Use when user asks to create a proposal, generate a quote, draft a contract, or prepare a client document.

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Proposal Generation

Goal

Create PandaDoc proposals for clients, either from structured information or by extracting details from sales call transcripts.

Inputs (One of the following)

Option A: Structured Data

Client First Name, Last Name, Email, Company
Project Title
4 Key Problems (brief)
4 Key Benefits (brief)
Project Duration
Investment Breakdown (Month 1, Month 2, Month 3+)

Option B: Call Transcript

Sales call transcript to extract all details from

Scripts

./scripts/create_proposal.py - Creates PandaDoc proposal via API
./scripts/read_sheet.py - Read client data from sheets if needed

Process

1. Gather Information

If user provides structured data, use directly
If user provides transcript, extract:
- Client info (name, company, email)
- Project context and title
- 4 main problems/pain points
- 4 proposed solutions/benefits
- Financial terms (duration, value, costs)
Ask for any missing critical information

2. Generate Content

Expand the 4 problems and 4 benefits into strategic paragraphs:

Problem Expansion Guidelines:

Use direct "you" language (not third-person)
Focus on revenue impact and dollar amounts
Be specific and actionable
Example: "Right now, your top-of-funnel is converting very poorly to booked meetings..."

Benefit Expansion Guidelines:

Address the client directly
Emphasize ROI and payback period
Focus on concrete deliverables

Also generate:

Slide Footer: "Confidential | [Company] Strategic Initiative | [Date]"
Contract Footer: "[Company]-[ProjectTitle]-[YYYY-MM]"
Created Date: Current date (YYYY-MM-DD)

3. Execute Proposal Creation

python3 ./scripts/create_proposal.py <<'EOF'
{
  "client": {
    "firstName": "...",
    "lastName": "...",
    "email": "...",
    "company": "..."
  },
  "project": {
    "title": "...",
    "problems": {
      "problem01": "[Expanded Problem 1]",
      "problem02": "[Expanded Problem 2]",
      "problem03": "[Expanded Problem 3]",
      "problem04": "[Expanded Problem 4]"
    },
    "benefits": {
      "benefit01": "[Expanded Benefit 1]",
      "benefit02": "[Expanded Benefit 2]",
      "benefit03": "[Expanded Benefit 3]",
      "benefit04": "[Expanded Benefit 4]"
    },
    "monthOneInvestment": "...",
    "monthTwoInvestment": "...",
    "monthThreeInvestment": "..."
  },
  "generated": {
    "slideFooter": "...",
    "contractFooterSlug": "...",
    "createdDate": "..."
  }
}
EOF

4. Send Follow-Up Email

Use Gmail to send HTML follow-up email:

Subject: "Re: [Brief Project Context] Discussion"
Opening: Thank them for discussing challenges
Body: 2-4 numbered sections with bold headers
Each section: Description + "Steps:" bullet points
Closing: "I'll send you a full proposal shortly..."
Signature: "Thanks, Nick"

5. Notify User

Provide the PandaDoc "internalLink" for review
Confirm follow-up email was sent

Output

PandaDoc proposal URL (for editing/review)
Follow-up email sent to client

Environment

Requires in .env:

PANDADOC_API_KEY=your_key

Schema

Inputs

Name	Type	Required	Description
`client_first_name`	string	Yes	Client first name
`client_last_name`	string	Yes	Client last name
`client_email`	string	Yes	Client email address
`client_company`	string	Yes	Client company name
`project_title`	string	Yes	Project title
`problems`	array	Yes	4 key problems (brief)
`benefits`	array	Yes	4 key benefits (brief)
`investment_breakdown`	object	Yes	Month 1, Month 2, Month 3+ pricing
`call_transcript`	string	No	Alternative: sales call transcript to extract details from

Outputs

Name	Type	Description
`pandadoc_url`	string	PandaDoc internal link for review
`email_sent`	boolean	Whether follow-up email was sent

Credentials

Name	Source
`PANDADOC_API_KEY`	.env

Composable With

Skills that chain well with this one: onboarding-kickoff, welcome-email

Cost

PandaDoc API call (free tier available)

View source ↗

File metadata

name: create-proposal
description: Generate PandaDoc proposals from client information or sales call transcripts. Use when user asks to create a proposal, generate a quote, draft a contract, or prepare a client document.

View original text

---
name: create-proposal
description: Generate PandaDoc proposals from client information or sales call transcripts. Use when user asks to create a proposal, generate a quote, draft a contract, or prepare a client document.
---

# Proposal Generation

## Goal
Create PandaDoc proposals for clients, either from structured information or by extracting details from sales call transcripts.

## Inputs (One of the following)

**Option A: Structured Data**
- Client First Name, Last Name, Email, Company
- Project Title
- 4 Key Problems (brief)
- 4 Key Benefits (brief)
- Project Duration
- Investment Breakdown (Month 1, Month 2, Month 3+)

**Option B: Call Transcript**
- Sales call transcript to extract all details from

## Scripts
- `./scripts/create_proposal.py` - Creates PandaDoc proposal via API
- `./scripts/read_sheet.py` - Read client data from sheets if needed

## Process

### 1. Gather Information
- If user provides structured data, use directly
- If user provides transcript, extract:
  - Client info (name, company, email)
  - Project context and title
  - 4 main problems/pain points
  - 4 proposed solutions/benefits
  - Financial terms (duration, value, costs)
- Ask for any missing critical information

### 2. Generate Content
Expand the 4 problems and 4 benefits into strategic paragraphs:

**Problem Expansion Guidelines:**
- Use direct "you" language (not third-person)
- Focus on revenue impact and dollar amounts
- Be specific and actionable
- Example: "Right now, your top-of-funnel is converting very poorly to booked meetings..."

**Benefit Expansion Guidelines:**
- Address the client directly
- Emphasize ROI and payback period
- Focus on concrete deliverables

**Also generate:**
- Slide Footer: "Confidential | [Company] Strategic Initiative | [Date]"
- Contract Footer: "[Company]-[ProjectTitle]-[YYYY-MM]"
- Created Date: Current date (YYYY-MM-DD)

### 3. Execute Proposal Creation
```bash
python3 ./scripts/create_proposal.py <<'EOF'
{
  "client": {
    "firstName": "...",
    "lastName": "...",
    "email": "...",
    "company": "..."
  },
  "project": {
    "title": "...",
    "problems": {
      "problem01": "[Expanded Problem 1]",
      "problem02": "[Expanded Problem 2]",
      "problem03": "[Expanded Problem 3]",
      "problem04": "[Expanded Problem 4]"
    },
    "benefits": {
      "benefit01": "[Expanded Benefit 1]",
      "benefit02": "[Expanded Benefit 2]",
      "benefit03": "[Expanded Benefit 3]",
      "benefit04": "[Expanded Benefit 4]"
    },
    "monthOneInvestment": "...",
    "monthTwoInvestment": "...",
    "monthThreeInvestment": "..."
  },
  "generated": {
    "slideFooter": "...",
    "contractFooterSlug": "...",
    "createdDate": "..."
  }
}
EOF
```

### 4. Send Follow-Up Email
Use Gmail to send HTML follow-up email:
- Subject: "Re: [Brief Project Context] Discussion"
- Opening: Thank them for discussing challenges
- Body: 2-4 numbered sections with bold headers
- Each section: Description + "Steps:" bullet points
- Closing: "I'll send you a full proposal shortly..."
- Signature: "Thanks, Nick"

### 5. Notify User
- Provide the PandaDoc "internalLink" for review
- Confirm follow-up email was sent

## Output
- PandaDoc proposal URL (for editing/review)
- Follow-up email sent to client

## Environment
Requires in `.env`:
```
PANDADOC_API_KEY=your_key
```

---

## Schema

### Inputs
| Name | Type | Required | Description |
|------|------|----------|-------------|
| `client_first_name` | string | Yes | Client first name |
| `client_last_name` | string | Yes | Client last name |
| `client_email` | string | Yes | Client email address |
| `client_company` | string | Yes | Client company name |
| `project_title` | string | Yes | Project title |
| `problems` | array | Yes | 4 key problems (brief) |
| `benefits` | array | Yes | 4 key benefits (brief) |
| `investment_breakdown` | object | Yes | Month 1, Month 2, Month 3+ pricing |
| `call_transcript` | string | No | Alternative: sales call transcript to extract details from |

### Outputs
| Name | Type | Description |
|------|------|-------------|
| `pandadoc_url` | string | PandaDoc internal link for review |
| `email_sent` | boolean | Whether follow-up email was sent |

### Credentials
| Name | Source |
|------|--------|
| `PANDADOC_API_KEY` | .env |

### Composable With
Skills that chain well with this one: `onboarding-kickoff`, `welcome-email`

### Cost
PandaDoc API call (free tier available)

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
create_proposal.py uses a hardcoded TEMPLATE_UUID that may not exist in another PandaDoc account; SKILL.md does not explain how to obtain, verify, or override it.

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1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & trust

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

License: MIT
Version: 1.0.0
Last GitHub push: Aug 27, 2026
Registry updated: Sep 1, 2026
Instruction path: create-proposal/SKILL.md

Version reported in registry metadata; check source releases before relying on it.

Quality

58/100

Promising

Trust

51/100

Do not auto-install

Audit

70/100

Needs review

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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}

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Source: aiagentwithdhruv/skills
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[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/aiagentwithdhruv-create-proposal?metric=trust&label=Trust)](https://www.openagentskill.com/skills/aiagentwithdhruv-create-proposal?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/aiagentwithdhruv-create-proposal?metric=audit&label=Audit)](https://www.openagentskill.com/skills/aiagentwithdhruv-create-proposal/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/aiagentwithdhruv-create-proposal?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/aiagentwithdhruv-create-proposal?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

Preview badge Open audit Creator kit

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.