Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/wolfenazz/yzpzcode/discovery-question-formnpx skills add wolfenazz/YzPzCode --skill discovery-question-formgit clone --depth 1 https://github.com/wolfenazz/YzPzCodeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wolfenazz/yzpzcode/discovery-question-form)<a href="https://agentmods.dev/skills/wolfenazz/yzpzcode/discovery-question-form"><img src="https://agentmods.dev/badge/skills/wolfenazz/yzpzcode/discovery-question-form.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00015 | $0.00493 |
| Opus 5 | $0.00008 | $0.00246 |
| Sonnet 5 | $0.00003 | $0.00099 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
discovery-question-form scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Discovery question form
When the user's brief is ambiguous, the agent's first turn must surface
the smallest possible set of clarifying questions that unblock the rest
of the workflow. The questions are rendered as a structured form
(GenUI surface kind: form, persist tier: conversation so a follow-
up turn doesn't re-ask).
When to fire
- Brief is missing audience, target medium, or core intent.
- Brief explicitly invites questions ("ask me anything if unclear").
- The discovery skill or pipeline declares a
discoverystage.
Emission shape
Emit the form as a question-form block whose body is a JSON object with a
top-level questions array. Do not emit a bare question object by itself; the
renderer only recognizes the wrapped form contract.
<question-form id="discovery" title="Quick brief — 30 seconds">
{
"description": "I'll lock these in before building. Skip what doesn't apply — I'll fill defaults.",
"questions": [
{
"id": "audience",
"label": "Who's the primary audience?",
"type": "checkbox",
"options": ["VC", "Customer", "Internal team"],
"maxSelections": 2,
"required": true
}
]
}
</question-form>
Question object shape
Each entry in the top-level questions array uses:
id: stable answer key, for exampleaudience.label: user-facing question copy.type: one ofradio,checkbox,select,text, ortextarea.options: required for choice controls; strings are allowed, or objects with localizedlabeland stablevalue.maxSelections: include this for checkbox controls with a limited selection count.required: set totrueonly when the answer is needed before work can continue.
Convergence
The discovery atom completes when every required question has an answer
in genui_surfaces for the current conversation. The agent should not
loop back to discovery after that — the same surface id renders cached
on the next turn.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 67 lines · 15 tokens per session scan A a3bac0b3d8b0
discovery-question-form is a skill published in the GitHub repository wolfenazz/YzPzCode (13 stars, last pushed yesterday), licensed Apache-2.0. It adds 15 tokens to every session and 493 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
orca-cli
Use the public orca CLI to operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and the browser embedded inside the Orca app. Use when the user says "$orca-cli", "use orca cli", "Orca worktree", "child worktree", "cardStatus", "spawn codex/claude…
tokf-discover
Find missed token savings by scanning AI coding session files for commands that ran without tokf filtering.
slackcli
Read, send, search, and manage Slack workspaces with the slackcli binary. Installs slackcli if missing and checks or proposes authentication first. Use whenever the user mentions Slack or pastes a slack.com link.
alive:system-cleanup
The world feels messy. Stale tasks, orphan folders, v2 remnants, unsaved sessions — entropy is accumulating and needs to be addressed before it compounds. Scans across all walnuts, then surfaces issues one at a time.
alive:world
The human doesn't know what to work on, or wants to see everything at once. They need the big picture — what's active, what's stale, what needs attention. Renders a live world view grouped by ALIVE domain, then routes to open, tidy, find, history, or map.
alive:bundle
Create, share, and graduate bundles — the unit of focused work within a walnut. Manages the full bundle lifecycle from creation through sharing to graduation.