agent-experience

What this skill does

# Audit Agent Experience

Evaluate how well a product/SDK/docs surface works when an AI agent actually tries to onboard and do a realistic task — **starting from a short one-sentence prompt**, with nothing pasted in. The agent must find the docs, install what it needs, and attempt real work. That's the only honest test of agent DX.

The skill spawns multiple subagents in parallel, captures each one's tool-call trace, and scores the experience using the same dimensions as the Skill Test Arena dashboard: Setup Friction, Speed, Efficiency, Error Recovery, Doc Quality.

## Core principle

**Do not spoonfeed.** The subagent gets a tiny prompt like *"Get started with {product} and {do its primary thing}"*. It must discover the docs, choose the path, and hit real failures. A good doc survives this; a bad doc does not.

## Workflow

Execute these steps in order. Do not skip ahead.

### Step 1 — Identify the target and define the abstract goal

Resolve what the user is asking to audit. The target may arrive in one of three forms:

- **URL** — a docs site or product page (e.g., `https://docs.example.com`). This is the *seed* the subagents start from.
- **Repo / file path** — for SKILL.md audits or SDK repos.
- **Product name** — if the user is vague ("test my product"), ask via `AskUserQuestion` for the URL or repo.

**This skill is product-agnostic. Never assume what the user wants to audit.** Do not infer a target from environment signals (operator's email domain, git remote, repo name, recent files, memory, CLAUDE.md). Even if context strongly suggests a particular company, the user-facing question must NOT pre-fill or default to any specific product, URL, or company name. Ask open-endedly with neutral options only: e.g., "Paste a URL", "Paste a local path", "Type a product name". If the user did not name a target in their invocation, ask them — start fresh, no priors.

**Research lightly** *after* the user has named a target. 1 WebFetch max, enough to confirm: what is this product, and does it have a getting-started guide? You're identifying *that there is a flow to follow*, not extracting the steps. The whole point is to let the docs dictate the path.

**Define ONE abstract goal, not a step-by-step checklist.** The goal should be at the level of "complete the onboarding" or "make the product do its primary thing once" — NOT a list of specific actions.

Why: prescriptive checklists steer agents. If you tell them "navigate to example.com" but the docs' quickstart navigates to a different URL, the agent is torn between your instruction and the docs. That pollutes the test.

Examples of good abstract goals (the target product is supplied by the user — the examples below are illustrative only, not defaults):
- A search API → *"Complete the getting-started guide. Success = your code successfully calls the API and prints whatever the docs treat as a meaningful result."*
- A payments API → *"Complete the getting-started flow for making a test charge. Success = you have a charge ID or equivalent confirmation."*
- A browser-automation SDK → *"Complete the getting-started guide end-to-end. Success = you have code that runs a cloud browser session using whatever approach the docs recommend."*
- A SKILL.md → *"Follow the skill's instructions and produce a successful outcome for its advertised job."*

Examples of BAD goals (too prescriptive — don't do this):
- ~~"Navigate to https://example.com"~~ (steers — the docs may pick a different URL)
- ~~"Use Playwright"~~ (the docs may recommend Stagehand or Selenium)
- ~~"Print the page title"~~ (the docs may print session ID, response body, anything)

The subagent will self-report against the abstract goal: *did I complete the onboarding as the docs described?* (yes / no / partial). The concrete sub-outcomes the agent *actually achieved* live in their trace under `primary_outcome_achieved`, not in a pre-defined checklist.

If the target has no clear getting-started flow (rare — even a README is a flow), ask the user what "done" means before continuing.

### Step 2 — Gather audit config via AskUserQuestion

Use `AskUserQuestion` in a **single call with 4 questions**. Options: max 4 per question.

1. **Test depth** (single-select, header: `"Depth"`):
   - `5 agents (Recommended)` — balanced coverage
   - `3 agents` — quick sanity check
   - `10 agents` — thorough, higher cost

2. **Programming languages** (multiSelect, header: `"Languages"`): pick up to 4 — `Python`, `TypeScript`, `Go`, `Shell/Bash` (let user deselect).

3. **Personas** (multiSelect, header: `"Personas"`):
   - `Standard (Recommended)` — neutral baseline, no behavioral flavoring. Just "do the task." Best for unbiased measurement.
   - `Pragmatic` — just get it working, fastest path
   - `Thorough` — read the docs end-to-end before coding
   - `Skeptical` — verify claims the docs make

4. **Execution mode** (single-select, header: `"Exec mode"`):
   - `Allow Bash (Recommended)` — subagents can run `npm install`, `curl`, etc. on your machine. Most realistic.
   - `Draft-only` — subagents may fetch docs and write code but won't execute anything. Safer.

After the user answers, gather one more question about model choice:

5. **Model** (single-select, header: `"Model"`):
   - `Sonnet (Recommended)` — balanced cost/quality, default for most audits
   - `Opus` — strongest reasoning, highest cost; good for dense/ambiguous docs
   - `Haiku` — cheapest, fastest; good for checking if docs are agent-friendly to smaller models
   - `Mixed comparison` — split agents across Opus + Sonnet + Haiku so you can see how doc quality varies by model size. Useful for "are my docs robust even to weaker models?"

Pass the chosen model to each `Agent` invocation via the `model` parameter. If `Mixed`, distribute N agents roughly equally across the 3 models (round-robin by slot index) and record which model each agent used in the trace + report.

After the user answers, you have: `depth` (N), `languages[]`, `personas[]`, `exec_mode`, `model`.

**If `exec_mode = "Allow Bash"`**, follow up with a second AskUserQuestion asking about credentials:

- **Credentials** (single-select, header: `"Credentials"`):
  - `Auto-discover (Recommended)` — skill checks the user's env vars, common dotfiles, and credential managers; only prompts for paste if nothing found. Best for repeat use and for cases where another operator is running the audit.
  - `None — let agents block (friction test)` — agents hit the credential wall, counts as Setup Friction. Best for pure docs audits.
  - `Paste manually` — you paste keys directly; skill injects them. Use when you don't have keys stored locally yet.

If user picks `Auto-discover`, run **Step 2.5** below before continuing. If `Paste manually` (or auto-discover falls back), AskUserQuestion asks for the credential **values** — not the names. The skill then writes them to each workspace `.env` using **generic, product-agnostic names**:

- Primary credential → `API_KEY`
- Secondary (e.g. project/org ID) → `PROJECT_ID`
- Third (e.g. webhook secret) → `SECRET`

**Do NOT use product-specific names** like `BROWSERBASE_API_KEY`, `EXA_API_KEY`, `STRIPE_SECRET_KEY`. Those names steer the agent — they see `BROWSERBASE_API_KEY` in env and skip ever reading the docs to find out what env var the SDK actually expects. The generic name forces them to:

1. Read the docs to discover the product's actual env var name (e.g. `BROWSERBASE_API_KEY`).
2. Map the generic `API_KEY` value into whatever form the SDK requires — either re-export (`export BROWSERBASE_API_KEY=$API_KEY`) or pass inline in code (`new Browserbase({ apiKey: process.env.API_KEY })`).

If an agent fails to figure out the mapping, that's a doc quality signal — the docs weren't clear about credential naming.

### Step 2.5 — Credential auto-discovery (only if user picked `Auto-discover`)

Run a tiered lookup. **Stop at the first tier that produces a usable candidate.** Never print credential values to chat — only name