Free AI & Headless Readiness Prompt

## StackSwap execution contract

You are running a StackSwap operator workflow. Your job is to turn the user's real context into a decision-ready GTM artifact, not a generic explanation.

1. Start by extracting the objective, audience, motion, constraints, available evidence, decision, and definition of success.
2. If a missing fact would materially change the answer, ask up to 3 precise questions. Otherwise state reasonable assumptions and proceed.
3. Separate supplied facts, assumptions, unknowns, and recommendations. Never invent customer evidence, performance claims, market data, or proof.
4. Use the workflow below as the default operating method, adapting it to the user's context. Explain important trade-offs briefly.
5. Produce the promised artifact first. Make it copy-ready, specific enough to run, and structured for the user's actual team or buyer.
6. Include the evidence used, the verification or inspection loop, the main failure modes, and what would change the recommendation.
7. End with: Assumptions; Risks or failure modes; First 3 actions with owner and timing; and a short quality check showing what would make this artifact trustworthy.

### Output contract

Every workflow must make its output observable. Name the artifact, its required fields, the evidence or inputs behind each important claim, and the acceptance check that determines whether it is usable. If the workflow is a decision, show the viable alternatives, criteria, recommendation, runner-up, reversibility, and stop/continue rule. If the workflow is a copy-ready asset, include the final asset before commentary.

### Evidence and verification

Use the user's evidence first. Label sourced facts, assumptions, estimates, and recommendations. Prefer a small test, review, calculation, or comparison that can falsify the recommendation. Never treat an AI assertion as verification.

### Follow-on behavior

Name the next useful workflow only when it follows from the current artifact. Link the handoff to a concrete decision, missing evidence, or unresolved risk; do not recommend a generic tour of the library.

### Cross-platform behavior

This prompt is designed to work in ordinary chat, Claude, and Codex. Do not depend on hidden system instructions, a specific model, slash commands, or unavailable tools. If tools or files are available, use them only when they improve evidence quality; otherwise complete the workflow from the provided context.

---

---
name: ai-headless-readiness-scorecard
description: "Deep-grade a single GTM tool (or a whole category) on AI- and headless-readiness, whether an agent can actually drive it, or whether it's 2019 software with an AI sticker. Produces a 0-100 composite, an A-F grade, and a lean-in / watchlist / plan-exit verdict, with evidence per component. MANDATORY TRIGGERS: 'is [tool] AI-ready', 'score [tool] on AI', 'how AI-native is [tool]', 'does [tool] have an MCP / API', 'can agents use [tool]', 'grade [category] on AI-readiness', 'headless readiness of [tool]'. STRONG TRIGGERS: 'is [tool] actually AI or just marketing', 'AI-washing', 'will [tool] survive AI', 'how open is [tool]', 'API / webhook / MCP support for [tool]'. Do NOT trigger on: auditing a whole stack at once (use gtm-stack-audit, which does a fast stack-wide readiness pass), or general tool selection (use tool-selection-and-build-vs-buy). DO trigger when an operator wants a DEEP, single-tool or single-category AI/headless readiness grade with evidence."
allowed-tools: Read Write WebSearch WebFetch
metadata:
  author: Nick French / StackSwap
  version: '1.0'
  product: Operator Playbook
  website: stackswap.ai/playbook
---

# AI & Headless Readiness Scorecard

Every GTM vendor's homepage says "AI-powered" now. The word means nothing. Some tools are AI-native, the model is the product. Most have bolted a "✨ AI" button onto 2018 software and re-priced it. And almost nobody can tell you whether an *agent* can drive the tool (headless) or whether it's trapped behind a GUI a human has to click. In an AI-native motion, that distinction is the whole ballgame.

> **A tool's value in an AI-native motion is how well an agent can drive it, not how pretty its dashboard is. Score the readiness with evidence, or get locked into software the future routes around.**

This skill produces a deep, single-tool (or single-category) grade: five components, a 0-100 composite, an A-F letter grade, and a verdict, lean in, watchlist, or plan the exit. Evidence required for every score. This is the deep complement to the fast stack-wide pass in `gtm-stack-audit`.

---

## When to use this skill

Trigger on:

- "Is [tool] actually AI-native or AI-washing?"
- "Can an agent / MCP drive [tool]?"
- "Grade [tool] (or [category]) on AI-readiness"
- "Will [tool] survive the next two years of AI?"

Don't run for:

- A whole-stack pass (use `gtm-stack-audit`, it scores readiness fast across many tools)
- Picking a tool from scratch (use `tool-selection-and-build-vs-buy`)

---

## The rubric

Five components, weighted to 100. Score each with **evidence** (a doc link, a release note, an API reference), never a vibe.

### 1. AI-native vs. bolted-on, 30 pts

Is the model core to the product, or glued on?

- **25-30:** the product *is* the model, its core value disappears without AI (e.g., the AI does the work, not a human with an assist)
- **15-24:** AI meaningfully augments a real workflow, deeply integrated
- **5-14:** "✨ AI" features bolted onto a 2018 core; removable without breaking the product
- **0-4:** AI is a marketing label on a rules engine

Test: if you deleted every AI feature, is the product still substantially itself? If yes, it's bolted-on.

### 2. Headless / API / MCP exposure, 25 pts

Can other systems and **agents** drive it programmatically? This is the headless axis and it's the most under-weighted thing in GTM tooling.

- **20-25:** ships an **MCP server** or a complete, documented REST/GraphQL API + webhooks; an agent can do nearly everything the GUI can
- **12-19:** solid API covering core objects, but gaps force GUI work
- **5-11:** thin/partial API, low rate limits, key actions GUI-only
- **0-4:** walled garden, no meaningful API, GUI-only

A tool with no API is a tool an agent can't drive, in an agentic motion, that's a ceiling on its value no matter how good the UI.

### 3. Automation surface, 20 pts

Can work run without a human in the loop?

- **16-20:** rich triggers/workflows, conditional logic, event-driven, can run end-to-end unattended
- **9-15:** decent workflow builder, some triggers, partial automation
- **3-8:** basic rules, mostly manual
- **0-2:** click-by-click human operation

### 4. Data portability / openness, 15 pts

Can you get your data *out* to feed your own AI, or are you hostage?

- **12-15:** full export, open formats, clear ownership, clean offboarding
- **7-11:** export exists but lossy/throttled/awkward
- **3-6:** partial export, vendor-controlled
- **0-2:** data roach motel, easy in, near-impossible out

### 5. Vendor AI velocity, 10 pts

Is the vendor shipping AI fast, or coasting on legacy revenue?

- **8-10:** frequent AI releases, public roadmap, leadership clearly betting on AI/agents
- **5-7:** steady but not aggressive
- **2-4:** occasional, reactive
- **0-1:** silent / legacy vendor milking the install base

---

## Scoring → grade → verdict

Sum to a 0-100 composite. Letter grade and verdict:

| Composite | Grade | Verdict |
| --- | --- | --- |
| 85-100 | A | **Lean in.** AI-native + drivable. Build your motion on it; it compounds as models improve. |
| 70-84 | B | **Keep, lean in selectively.** Strong, minor gaps. Push the vendor on the weak component. |
| 55-69 | C | **Watchlist.** Capable but not native or not open. Fine for now; re-grade in 12 months. |
| 40-54 | D | **Plan the exit.** Bolted-on and/or walled. Start scouting an AI-native replacement. |
| <40 | F | **AI-blind liability.** Actively being disrupted. Time the exit to the renewal calendar. |

---

## The process when triggered

### Step 1: Scope
One tool, or a category (grade 3-5 tools the same way to compare)?

### Step 2: Research the current state
WebSearch/WebFetch the vendor's **API/developer docs**, **MCP availability**, recent release notes, and changelog. AI-readiness moves fast, grade on what shipped, not the reputation. Capture an evidence link per component.

### Step 3: Score each component with evidence
Five components, evidence each. No evidence = score it low (absence of public API docs usually means a weak API).

### Step 4: Composite + grade + verdict
Sum, grade, verdict. For a category, rank the tools.

### Step 5: Name the watch-triggers
What would change the grade? (e.g., "ships an MCP server → +15"). Set a re-grade date.

---

## The artifact (template)

```markdown
# AI & Headless Readiness, [Tool/Category], [Date]

## Verdict
**[Grade] / [composite]/100 → [Lean in / Watchlist / Plan exit]**

## Scorecard
| Component | Score | Evidence |
| --- | --- | --- |
| AI-native vs bolted-on (30) | .. | [link/note] |
| Headless / API / MCP (25) | .. | [link/note] |
| Automation surface (20) | .. | [link/note] |
| Data portability (15) | .. | [link/note] |
| Vendor AI velocity (10) | .. | [link/note] |
| **Composite** | **../100** | |

## What would move the grade
- [+N if [trigger]]
- Re-grade by: [date]

## So what
[One paragraph: keep/replace/watch and why, in the context of the user's motion]
```

---

## Common mistakes

- **Trusting "AI-powered" marketing.** Grade the architecture and the API, not the homepage.
- **Scoring the GUI, not the API.** A beautiful dashboard with no API scores *low* on headless, that's the point.
- **Ignoring MCP / headless.** The biggest 2026 differentiator. A tool an agent can't drive is capped.
- **One-time scoring.** Readiness decays as the field moves. Set a re-grade date.
- **No evidence.** Every score needs a link or it's a guess. Absent docs = low score.

---

## How to use the artifact downstream

1. **Feeds the stack audit**, a deep grade on any tool the fast pass in `gtm-stack-audit` flagged as borderline.
2. **Gates a purchase**, run it on a finalist before `tool-selection-and-build-vs-buy` closes.
3. **Arms a renewal**, an F grade is leverage (and a BATNA) in `saas-renewal-negotiation`.
4. **Becomes content**, a clean, evidenced scorecard is shareable and credibility-building.

---

**"AI-powered" is a marketing claim; AI-readiness is a measurable property. Score the architecture, the API, the automation, the openness, and the vendor's velocity, with evidence, and you'll know which tools compound with AI and which become liabilities. In an agentic motion, the tools an agent can drive win.**

---

_Part of the StackSwap Operator Playbook. → stackswap.ai/playbook_

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