interview

A guided interview for onboarding a new product or project. It helps collect information about the product's purpose, direction, main success measure, and competitors.

In plain words
What is it for?
Use it to fill in a product canvas through a structured discovery conversation, while following rules for reading and updating canvas files.
Why use it?
It prevents important project context from being guessed or scattered before work begins.

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

Made for: Claude Code, Codex.

Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,263 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. 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 $0.00026 $0.10263
Opus 5 $0.00013 $0.05131
Sonnet 5 $0.00005 $0.02053
Haiku 4.5 $0.00003 $0.01026

Measured 2d ago against content hash 0805b1dcb7da, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

interview scanned grade B with 1 finding 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.

Reads agent configuration directoriesmediumAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

grep -o "<prefix>-[0-9][0-9]*" .claude/canvas/<file>.yml | sort -u -t- -k2 -n | tail -3
plugins/mycelium/skills/interview/SKILL.md · 519 lines

How it starts

The opening of the file, as written. The whole thing — 519 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Interview Skill

Progressive onboarding through structured discovery conversation.

Preflight: Read target canvas file(s) before any Write/Edit

Hard rule. Before issuing Write or Edit against any .claude/canvas/*.yml, use the Read tool on that file in this session. Claude Code's Read-before-Write check requires the Read tool specifically — cat/head/grep via Bash do NOT satisfy it.

Edit vs Write — different cost profiles (verified 2026-05-14):

  • Edit (exact-string replacement): Read with limit: 1 satisfies the check at ~50 tokens. State-tracking is per-file, not per-byte — subsequent Edit calls work anywhere in the file. Use this for partial updates against large canvas files (e.g., purpose.yml at 800+ lines).
  • Write (full replacement): do a full Read first. Write obliterates the file; you should see what you're about to replace. The limit:1 shortcut is not appropriate here.

ID-bearing entries — scan the ID space before assigning (added 2026-05-15, v0.23.19): When adding a new component, opportunity, solution, or any other ID-bearing entry to a canvas file, run a Bash grep first to confirm the next ID in your prefix sequence is actually free:

grep -o "<prefix>-[0-9][0-9]*" .claude/canvas/<file>.yml | sort -u -t- -k2 -n | tail -3

Replace <prefix> with the canvas's ID prefix (comp for landscape, opp for opportunities, sol for solutions, ht for human-tasks, etc.). Then pick the next free integer, matching the zero-padding already used in that file. The sort is NUMERIC (-t- -k2 -n) rather than lexical, and that is not pedantry: a plain sort -u orders ht-1 after ht-080, so on a canvas with inconsistent padding it reports the wrong maximum and the next ID collides. Verified on the dogfood repo 2026-08-13, where lexical sort returned ht-1 as the highest human-task ID against an actual ht-080. grep -o is also deliberate: it matches IDs wherever they appear, including cross-references and prose, so an ID that was promised somewhere but not yet defined is not handed out twice. validate_canvas.py has a duplicate-ID check (lines 230-239) that catches the failure on CI, but a duplicate can persist in the working tree for days if CI isn't run between edit and discovery — see roadmap-repo corrections.md 2026-05-15 "Duplicate canvas ID created in landscape.yml" for the worked example.

Read the full file on GitHub · 519 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 · 519 lines · 26 tokens per session scan B 0805b1dcb7da

Subscribe to this mod's changes

interview is a skill published in the GitHub repository haabe/mycelium (45 stars, last pushed 2d ago), licensed MIT. It adds 26 tokens to every session and 10,263 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

context-manager

Context management skill providing discovery, fetching, harvesting, extraction, compression, organization, cleanup, and guided workflows for project context.

darrenhinde/OpenAgentsControl · 27 tokens

prd-v05-technical-stack-selection

Determine technologies needed to build the product, making build/buy/integrate decisions during PRD v0.5 Red Team Review. Handles both greenfield and brownfield contexts. Triggers on requests to select tech stack, evaluate technologies, make build vs. buy decisions, discover existing assets, or when user asks "what…

mattgierhart/PRD-driven-context-engineering · 151 tokens

prd-v06-architecture-design

Define how system components connect, establishing boundaries, patterns, and integration approaches during PRD v0.6 Architecture. Triggers on requests to design architecture, create system design, define component relationships, or when user asks "design architecture", "system design", "how do components connect?"…

mattgierhart/PRD-driven-context-engineering · 115 tokens

prd-v08-monitoring-setup

Define monitoring strategy, metrics collection, and alerting thresholds during PRD v0.8 Deployment & Ops. Triggers on requests to set up monitoring, define alerts, or when user asks "what should we monitor?", "alerting strategy", "observability", "metrics", "SLOs", "dashboards", "monitoring setup". Outputs MON…

mattgierhart/PRD-driven-context-engineering · 89 tokens

prd-v08-runbook-creation

Create operational playbooks for incident response, deployments, and maintenance during PRD v0.8 Deployment & Ops. Triggers on requests to create runbooks, document procedures, or when user asks "how do we handle incidents?", "runbook", "operational procedures", "on-call guide", "incident response", "maintenance…

mattgierhart/PRD-driven-context-engineering · 87 tokens

prd-v09-feedback-loop-setup

Establish channels and processes for capturing and processing post-launch feedback during PRD v0.9 Go-to-Market. Triggers on requests to set up feedback systems, capture user input, or when user asks "how do we collect feedback?", "feedback loop", "user research", "post-launch feedback", "customer feedback", "NPS"…

mattgierhart/PRD-driven-context-engineering · 94 tokens