interview

A structured interview skill that asks questions before a feature is designed and saves the answers as a discovery brief. The brief records user goals, requirements, and constraints for later planning.

In plain words
What is it for?
Use it for discovery sessions, feature planning, and other work where the agent needs a clearer understanding of the intended feature.
Why use it?
It helps uncover missing context before implementation begins, reducing designs based on incomplete requirements.

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/skullninja/coco-workflow/interview
Any agent
npx skills add skullninja/coco-workflow --skill interview
Clone the repo
git clone --depth 1 https://github.com/skullninja/coco-workflow

Made for: Claude Code, Codex.

Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,314 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 $0.00032 $0.01314
Opus 5 $0.00016 $0.00657
Sonnet 5 $0.00006 $0.00263
Haiku 4.5 $0.00003 $0.00131

Measured 2d ago against content hash d142b2e2a9e0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 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.

skills/interview/SKILL.md · 120 lines

How it starts

The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Coco Interview Skill

Conduct a structured pre-design interview to gather deep user intent, constraints, and context before generating a feature design. Produces a discovery brief that feeds directly into the design skill.

When to Use

  • Pre-design discovery for Standard-tier features (before invoking the design skill)
  • Deep-dive during any planning session (strategic, tactical, operational, triage)
  • Natural language requests ("interview me about this feature", "let's discuss requirements")
  • When /coco:phase or /coco:planning-session tactical routes to Standard tier

Do NOT use for:

  • Trivial-tier features (use hotfix skill directly)
  • Light-tier features (skip interview, use design in light mode)
  • Features where the user has already provided exhaustive requirements

Setup

  1. Read .coco/config.yaml for project.specs_dir (default: specs).
  2. Determine the feature from conversation context:
    • If a feature name or description was provided, use it
    • If on a feature/* git branch, extract the feature name
    • If none of the above, ask the user what feature to discuss
  3. Determine the feature directory: {specs_dir}/{feature-name}/
  4. Load the discovery template from .coco/templates/discovery-template.md if it exists, otherwise use ${CLAUDE_PLUGIN_ROOT}/templates/discovery-template.md.
  5. Check if {specs_dir}/{feature-name}/discovery.md already exists:
    • If yes, enter refinement mode (see below)
    • If no, proceed with fresh interview

Execution

Category Progression

The interview covers these categories in order, adapting based on what the user has already provided:

  1. Problem & Motivation -- What problem does this solve? Who feels the pain? Why now?
  2. Users & Personas -- Who are the target users? What are their goals and skill levels?
  3. Scope & Boundaries -- What's explicitly in scope? What's out of scope? MVP vs future?
  4. Functional Requirements -- What are the core behaviors? What are the key user flows?
  5. Technical Context -- What existing systems does this interact with? Any constraints?
  6. UX & Interaction -- What does the user experience look like? Key screens/flows?
  7. Non-Functional Requirements -- Performance, security, accessibility, scalability?

Read the full file on GitHub · 120 lines

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. 2d ago First seen · 120 lines · 32 tokens per session scan A d142b2e2a9e0

Subscribe to this mod's changes

interview is a skill published in the GitHub repository skullninja/coco-workflow (7 stars, last pushed 2d ago), licensed MIT. It adds 32 tokens to every session and 1,314 once invoked, about $0.0002 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 2026-08-31.

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