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Interview

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danielmiessler
Interview

Evidence-grounded context refresh: reads constitutional files, TELOS, and CURRENT_STATE/IDEAL_STATE dimension files via TelosFreshness, pulls observed data (Oura sleep/HRV, Conduit app-time, work registry, git, expenses via StateEvidence), and drives a peer conversation that opens with claim-vs-evidence contradictions, drafts corrections for ratification, and closes with a ComputeGap-grounded ideal-state review. Routes to ContextCheckin; Phase0Setup on fresh install. USE WHEN /interview, resume interview, context check-in, telos check-in, what's stale, stale data, freshness check, update current state, update my current state, state sync, statusline says interview due, interview due, review TELOS, update ideal state, quarterly context refresh, fresh LifeOS install, configure DA name. NOT FOR single edits (Telos), bulk intake (Migrate), identity-only.

Overview

Publisherdanielmiessler
RepositoryLifeOS
Skill nameInterview
Stars
19K
Forks
2.5K
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by danielmiessler on GitHub. Read the source before you install it.

Installation

Install the Interview AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/danielmiessler/LifeOS.git /tmp/LifeOS
mkdir -p .claude/skills
cp -r /tmp/LifeOS/LifeOS/install/skills/Interview .claude/skills/Interview
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Interview in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Interview on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Interview is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Interview — constitutional-context peer conversation

What It Does

Interview reads your constitutional files — TELOS, identity, projects, system prompt, architecture — plus the CURRENT_STATE and IDEAL_STATE dimension files, checks their freshness, and confronts stale claims with observed data: Oura sleep/HRV/RHR, Conduit creation-vs-consumption time, the work registry, git cadence, and the expense ledger. The conversation opens with the sharpest claim-vs-evidence contradiction, drafts corrections you ratify, offers to populate the files the machinery can write, and closes with an ideal-state review grounded in the measured gap. On a fresh install it falls back to first-time setup. The 🎤 statusline chip (fed by InterviewDue.ts, daily launchd refresh) says when one is due; wrapping with --mark-done clears the cadence reason, and the skew/staleness reasons clear as their files are actually reviewed.

The Problem

The files that define who you are and what you're working toward drift out of date the moment you stop looking at them. A goal you set in January may be done, dead, or still right — but nobody re-reads the whole TELOS to find out, so the context the system runs on slowly rots. The usual fix is a blank "what's your mission?" prompt, which ignores everything already on file and makes you repeat yourself. This skill reads what's there first, flags only what's gone stale, and asks "still right?" instead of starting from zero.

How It Works

The skill reads the constitutional files via the freshness tooling, scores each section's staleness, and routes to the right workflow — a check-in on a populated system, or first-time setup on a fresh one. Staleness is a priority signal, not a failure flag.

Workflow Routing

WorkflowTriggerFile
ContextCheckindefault /interview on a populated system; "context check-in", "telos check-in", "what's stale", "how are we doing on…", "still on…", "review context"Workflows/ContextCheckin.md
Phase0Setupfresh install; DA name still reads "LifeOS"; PRINCIPAL_IDENTITY still reads "User"; PROJECTS sample-row only; .env missing required keysWorkflows/Phase0Setup.md
TelosCheckin (deprecated stub)back-compat for explicit "telos checkin" routingWorkflows/TelosCheckin.md (redirects to ContextCheckin)

Routing decision (run before either workflow):

bash
bun ~/.claude/LIFEOS/TOOLS/InterviewScan.ts --json | jq '[.targets[] | select(.phase == 0 and .completeness_score < 80)] | length'
  • > 0 → run Phase0Setup first, then ContextCheckin.
  • 0 → run ContextCheckin directly.

Quick Reference

  • The TELOS is on file. Read it before asking. Generic "what's your mission?" prompts are forbidden when TELOS is populated.
  • Staleness is information, not failure — a 95-day-old Goals section might still be right; the prompt is "still right?", not "you're behind."
  • Per-entry on typed-ID sections (G3, M0, P2…), section-level on prose (Current State, Sparks).
  • Bump on every approved edit: bun ~/.claude/LIFEOS/TOOLS/TelosFreshness.ts --bump <slug>. Without this the staleness signal degrades to noise.
  • Stop signals are sacred. "Enough" / "stop" / "later" exits gracefully. State persists in the file itself.
  • ID-stability rule: G3 stays G3 even when edited or dropped; new entries get the next sequential ID.

Gotchas

  • The current.json day-label trap. USER/HEALTH/current.json labels itself with today's day while its last_night block may carry the newest available sleep record, days older. Quote the evidence cache's latest_sleep_record_day, never the label.
  • Not every health source is live. The evidence panel (StateEvidence.ts) computes per-source liveness at run time — quote its live/dead map, never a remembered one. Claims whose only source is dead can be asked, not checked — say so instead of implying verification.
  • InterviewDue.ts --mark-done at wrap is what silences the 🎤 statusline chip. Skipping it leaves the chip nagging with the interview already done — the cadence clock reads MEMORY/STATE/interview.json, not file mtimes.
  • Migration must run once before TelosCheckin works. A TELOS without YAML frontmatter (no last_updated:) returns fileUpdated: null and every section reads as stale. Run bun ~/.claude/LIFEOS/TOOLS/MigrateTelosFreshness.ts once; idempotent and content-preserving (verifies sha256 of stripped content).
  • The slug is normalized: "Current State" → current_state, "Wrong (Things I've been wrong about)" → wrong, "2036 — A Day in the Life…" → 2036. Always run heading text through sectionSlug() from TelosFreshness.ts.
  • Pulse caches freshness for 60s. After bumping, the next /api/telos/freshness/summary call returns the cached value until invalidation. Send /reload (POST) to invalidate the cache immediately, or wait 60s.
  • TelosRenderer (GenerateTelosSummary.ts) preserves the markers. It splits by ^## headings; the per-section HTML comments live inside the section body and are not re-emitted in PRINCIPAL_TELOS.md. Safe to run after edits.
  • The scanner shares the freshness reader. InterviewScan.ts calls readTelosFreshness() once at startup and adds age_days, threshold_days, stale to every Phase 1+ target row. Stale sections get a +200 priority bump so they naturally rise in --next output.
  • Voice notifications are "only on actual writes." Don't voice-confirm every prompt — only after a real Edit lands. The voice channel is a low-frequency signal; preserving that is what makes it land when it matters.

Examples

  • "/interview" on a populated system → routing probe via InterviewScan.ts, then ContextCheckin walking the stalest sections first ("G2 is 95 days old — still right?").
  • "what's stale?" → freshness summary from TelosFreshness, prioritized list, no edits unless approved.
  • Fresh install (DA name still "LifeOS", PROJECTS sample-row only) → Phase0Setup first, then ContextCheckin.

Related

  • /Telos — edit a single TELOS section directly (without the conversational walk).
  • /Migrate — intake content from other sources (one-shot classification, not an interview).
  • an identity-profile skill — manage PRINCIPAL_IDENTITY directly.
  • Skill("ISA") — interview an ISA (different artifact, different workflow).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Interview AI skill do?

Evidence-grounded context refresh: reads constitutional files, TELOS, and CURRENT_STATE/IDEAL_STATE dimension files via TelosFreshness, pulls observed data (Oura sleep/HRV, Conduit app-time, work registry, git, expenses via StateEvidence), and drives a peer conversation that opens with claim-vs-evidence contradictions, drafts corrections for ratification, and closes with a ComputeGap-grounded ideal-state review. Routes to ContextCheckin; Phase0Setup on fresh install. USE WHEN /interview, resume interview, context check-in, telos check-in, what's stale, stale data, freshness check, update cu...

Why use Interview on TypingMind?

Because you install it once and use it with any model. Interview is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Interview in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/danielmiessler/LifeOS/tree/main/LifeOS/install/skills/Interview. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Interview?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Interview?

As many as you like. As long as a model supports skills, you can use Interview with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Interview AI skill free?

Yes. It is published on GitHub by danielmiessler under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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