addyosmani/agent-skills is a collection of reusable workflows, quality checks, commands, and other instructions that guide AI coding agents through software development. It is for developers who want agents to follow consistent engineering practices, and the catalogue entries are its packaged skills, commands, agents, plugins, instructions, and hooks.
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 skills add addyosmani/agent-skills --skill interview-megit clone --depth 1 https://github.com/addyosmani/agent-skillsWrote 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/skills/addyosmani/agent-skills/interview-me)<a href="https://agentmods.dev/skills/addyosmani/agent-skills/interview-me"><img src="https://agentmods.dev/badge/skills/addyosmani/agent-skills/interview-me/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/addyosmani/agent-skills/interview-me"><img src="https://agentmods.dev/badge/skills/addyosmani/agent-skills/interview-me.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 5.1 | $0.00108 | $0.03383 |
| Opus 5.5 | $0.00043 | $0.01353 |
| Sonnet 5.5 | $0.00022 | $0.00677 |
| Haiku 4.5 | $0.00011 | $0.00338 |
Grade A, and why
interview-me 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 1mo 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- interview-me — 100% identical, 10 lines differ
- interview-me — 100% identical, 10 lines differ
- interview-me — 100% identical, 10 lines differ
- interview-me — 100% identical, 10 lines differ
- interview-me — 100% identical, 11 lines differ
- interview-me — 100% identical, 10 lines differ
- interview-me — 100% identical, 10 lines differ
- interview-me — 100% identical, 10 lines differ
How it starts
The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview Me
Overview
What people ask for and what they actually want are different things. They ask for "a dashboard" because that's what one asks for, not because a dashboard solves their problem. They say "make it faster" without a number to hit.
The cheapest moment to find this gap is before any plan, spec, or code exists. Once you've started building, switching costs are real, and the user will rationalize the wrong thing into a "good enough" thing. The misfit gets locked in.
This skill closes the gap before it costs anything. The other Define-phase skills assume you already know roughly what you want: idea-refine generates variations from an idea, spec-driven-development writes the requirements down, doubt-driven-development stress-tests a plan after you've drafted one. Interview-me is the part before all of those, where you ask one question at a time, with your best guess attached, until you can predict what the user is going to say before they say it.
When to Use
Apply this skill when:
- The ask is missing at least one of: who the user is, why they want it, what success looks like, what the binding constraint is
- The request is conventional rather than specific ("build me X", "make it faster") and you can't unpack the convention without guessing
- You're tempted to start with assumptions you haven't surfaced
- The user hasn't said which value they're optimizing for when two reasonable ones are in tension (simplicity vs. flexibility, cost vs. speed)
- The user explicitly invokes: "interview me", "grill me", "before we start, are we sure?", "stress-test my thinking"
When NOT to use:
- The ask is unambiguous and self-contained ("rename this variable", "fix this typo")
- The user has explicitly asked for speed over verification
- Pure information requests ("how does X work?", "what does this code do?")
- Mechanical operations (renames, formats, file moves)
- You already have ≥95% confidence; re-read the stop condition below before assuming you don't
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.
- 1mo ago Changed · +10 lines ace4b675999b
- 1mo ago First seen · 226 lines · 108 tokens per session scan A 1d94741d10d2
interview-me is a skill published in the GitHub repository addyosmani/agent-skills (109,917 stars, last pushed 11d ago), licensed MIT. It adds 108 tokens to every session and 3,383 once invoked, about $0.0004 per session on Opus 5.5. 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.
Other skills, from other repositories
ai-output-validation
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and fallback handling.
think-before-coding
Forces explicit reasoning before writing any code. Surfaces assumptions, manages confusion, and prevents hallucination by demanding clarity upfront.
git-workflow
Trunk-based development with atomic commits, clean history, and meaningful commit messages. Every commit should be deployable.
idea-to-spec
Converts vague ideas into concrete, testable specifications with acceptance criteria. No implementation begins without a spec.
meeting-notes-to-tasks
Converts unstructured meeting notes into structured, assigned, time-bounded action items. Never leave a meeting without knowing who does what by when.
rag-and-memory
Patterns for Retrieval-Augmented Generation (RAG) and agent memory systems. Retrieves only relevant context, prevents context bloat, and maintains coherent state across sessions.