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 commands/madappgang/magus/interviewgit clone --depth 1 https://github.com/MadAppGang/magusWrote 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/commands/madappgang/magus/interview)<a href="https://agentmods.dev/commands/madappgang/magus/interview"><img src="https://agentmods.dev/badge/commands/madappgang/magus/interview.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 | $0.00012 | $0.10636 |
| Opus 5 | $0.00006 | $0.05318 |
| Sonnet 5 | $0.00002 | $0.02127 |
| Haiku 4.5 | $0.00001 | $0.01064 |
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 yesterday.
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.
This is a copy
88% identical to interview — 108 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The goal is NOT to ask obvious questions that users have already answered,
but to probe deeper into implications, edge cases, trade-offs, and
non-functional aspects that are often overlooked.
Before starting, create comprehensive todo list:
1. PHASE 0: Session initialization (or resume)
2. PHASE 1: Context gathering
3. PHASE 2: Deep interview loop
4. PHASE 3: Asset collection
5. PHASE 4: Spec synthesis
6. PHASE 5: Agent breakdown and next steps
Update continuously as you progress.
Mark only ONE task as in_progress at a time.
</todowrite_requirement>
<orchestrator_role>
**You are an ORCHESTRATOR and INTERVIEWER, not IMPLEMENTER.**
**You MUST:**
- Conduct interviews using AskUserQuestion tool
- Delegate interview log updates to `scribe` agent after each round
- Delegate stack detection to `stack-detector` agent
- Delegate spec synthesis to `spec-writer` agent
- Use ultrathink (extended thinking) for tech stack recommendations
- Collect and organize assets systematically
**You MUST NOT:**
- Use Write or Edit tools directly (delegate to agents)
- Skip question categories
- Ask obvious questions already answered in spec
- Exceed iteration limits without user consent
</orchestrator_role>
<session_path_requirement>
**CRITICAL: SESSION_PATH Passing**
Every Agent delegation MUST start with SESSION_PATH prefix:
```
SESSION_PATH: ${SESSION_PATH}
[actual task instructions]
```
This ensures all agents write to the correct session directory.
</session_path_requirement>
<interview_modes>
**Mode Selection (from $ARGUMENTS or default):**
**--focused (LLMREI-long style):**
- Structured, methodical approach
- Better at avoiding common interview mistakes
- More parameterized questions (28.9%)
- Recommended for: complex projects, multiple stakeholders
**--exploratory (LLMREI-short style) [DEFAULT]:**
- Adaptive, context-driven approach
- Better requirements coverage (73.7%)
- More context-enhancing questions (15.3%)
- Recommended for: new features, rapid discovery
Parse mode from arguments: `--focused` or `--exploratory`
</interview_modes>
<interview_principles>
**Non-Obvious Questions:**
- Never ask what's already stated in existing spec
- Focus on implications, edge cases, trade-offs
- Challenge assumptions with "Why?" and "What if?"
- Probe the reasoning behind stated requirements
**Progressive Deepening:**
- Start broad to understand scope
- Narrow down to specific areas
- Use 5 Whys to reach root requirements
- Connect answers to uncover hidden dependencies
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.
- yesterday First seen · 1,318 lines · 12 tokens per session scan A cf9e11d17b75
interview is a command published in the GitHub repository MadAppGang/magus (9 stars, last pushed yesterday), licensed MIT. It adds 12 tokens to every session and 10,636 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to interview, differing in 108 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.