knack-interview

A guided interview for teaching Knack, a tool for storing reusable AI-agent skills, how to perform a recurring task. It collects the task’s steps, examples, decision rules, and exceptions.

In plain words
What is it for?
Use it to document tasks such as support triage by describing the workflow, providing examples, explaining judgment calls, and saving the resulting skill.
Why use it?
It turns personal working knowledge into written instructions that an AI agent can reuse.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/jordan-gibbs/knack-cli/interview
Any agent
npx skills add jordan-gibbs/knack-cli --skill interview
Clone the repo
git clone --depth 1 https://github.com/jordan-gibbs/knack-cli

Made for: Claude Code, Codex.

Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 909 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00062 $0.00909
Opus 5 $0.00031 $0.00454
Sonnet 5 $0.00012 $0.00182
Haiku 4.5 $0.00006 $0.00091

Measured 3d ago against content hash b61ee424157d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

knack-interview 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 3d 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.

skills/interview/SKILL.md · 96 lines

How it starts

The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Knack Interview

You are conducting an interview with a user to extract a skill they want to teach to AI. Your job is to walk them through six phases, gather what each phase needs, and call the Knack CLI when each phase is complete to persist the state.

The user is using your agent surface (Claude Code, Cursor, Codex, etc.) inside their normal project. They have Knack installed. You are the LLM running the interview — there is no server LLM. The Knack CLI is your tool plumbing: it stores session state, writes the eventual SKILL.md to disk, and pushes the result to either GitHub or Knack Cloud depending on the user's configuration.

The six phases

  1. Genesis — establish what the task is, when it happens, what the end-to-end looks like. Load genesis.md for the rules of this phase.
  2. Artifacts — collect concrete example inputs and outputs from past instances. Load artifacts.md.
  3. Intuition — extract rules, priorities, and exceptions through scenario probing. Load intuition.md for the phase rules. The captured rules are appended directly into the draft SKILL.md's ## Intuition section (under ### Always / ### Except when / ### Edge cases). There is no separate intuition.md output file.
  4. Compile — generate the first draft SKILL.md from what you've learned. No separate prompt file: synthesize from the captured state.
  5. Refine — read the draft back to the user, iterate on critiques. Load refine.md.
  6. Publish — confirm the skill is ready and run knack publish <slug> to write it to their configured backend.

Operating rules

  • One question per turn. Never stack questions.
  • The user is a non-coder. Plain prose, sentence case, no jargon.
  • Don't summarize back to them unless asked.
  • Don't propose a solution before Compile.
  • Use their words, not technical vocabulary. No "workflow", "pipeline", "process" — use what they said.

Session state

Every interview is a session. Persist state between phases by calling:

Read the full file on GitHub · 96 lines

Files

What ships with it

4 files 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.

Changes

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.

  1. 3d ago First seen · 96 lines · 62 tokens per session scan A b61ee424157d

Subscribe to this mod's changes

knack-interview is a skill published in the GitHub repository jordan-gibbs/knack-cli (20 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 909 once invoked, about $0.0003 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 2026-08-30.

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