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 skills add redhuntlabs/wizard --skill intuitive-interviewinggit clone --depth 1 https://github.com/redhuntlabs/wizardWrote 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/redhuntlabs/wizard/intuitive-interviewing)<a href="https://agentmods.dev/skills/redhuntlabs/wizard/intuitive-interviewing"><img src="https://agentmods.dev/badge/skills/redhuntlabs/wizard/intuitive-interviewing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/redhuntlabs/wizard/intuitive-interviewing"><img src="https://agentmods.dev/badge/skills/redhuntlabs/wizard/intuitive-interviewing.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00047 | $0.01491 |
| Opus 5.5 | $0.00019 | $0.00596 |
| Sonnet 5.5 | $0.00009 | $0.00298 |
| Haiku 4.5 | $0.00005 | $0.00149 |
Grade A, and why
intuitive-interviewing 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intuitive Interviewing
What this does
Turns a 12-phase interview into a context-aware conversation. Detects what the user already said, picks the right depth, matches against known workflow shapes, drafts a strawman early, and pivots when scope shifts.
When to use
- Inside
building-a-spell(Stage 1) - Inside
refining-a-spell(when capturing what changed) - Anywhere a user-facing requirements interview happens
What you bring (Inputs)
A context dictionary from the calling skill, containing whatever was already extracted (from the user's trigger phrase + any prior turns).
What you get (Output)
A filled interview: every required frontmatter field has a value, every required body section has content, plus any kind-specific extras.
How it works (Steps)
This is a workflow with explicit stages and gates.
Stages
Stage A: Pick depth
Inspect the context dictionary. Pick depth as follows:
| If... | Use depth |
|---|---|
| User explicitly said "quick", "simple", "just" | Express |
| Context dictionary has 4+ extracted fields already | Express |
Kind is discipline |
Deep (always) |
User is first-time (no $WIZARD_HOME content) |
Standard |
| Otherwise | Standard |
Allow user override: "Tell me which depth you'd like (express / standard / deep)" — but only if depth was ambiguous.
Output handed to next stage: chosen depth.
Stage B: Match against workflow shapes
Read all shapes under skills/building-a-spell/workflow-shapes/. Score each by overlap of:
- Trigger keywords vs context dictionary's
nameanddescription - Output type vs context dictionary's
output - Step count and shape
If top score >= 0.7, propose it as a strawman:
"This looks like a
<shape-name>. Here's a draft. What would you change?"
If top score < 0.7, skip to Stage C.
Output handed to next stage: strawman draft (or none).
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.
- 1mo ago First seen · 166 lines · 47 tokens per session scan A 2e3fe412cbdb
intuitive-interviewing is a skill published in the GitHub repository redhuntlabs/wizard (11 stars, last pushed 6mo ago), licensed MIT. It adds 47 tokens to every session and 1,491 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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