Borrowing it
Nothing to install: this file belongs to Orinks/AccessiWeather. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Orinks/AccessiWeather/main/.codex/skills/deep-interview/SKILL.mdgit clone --depth 1 https://github.com/Orinks/AccessiWeatherWrote 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/orinks/accessiweather/deep-interview)<a href="https://agentmods.dev/skills/orinks/accessiweather/deep-interview"><img src="https://agentmods.dev/badge/skills/orinks/accessiweather/deep-interview/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/orinks/accessiweather/deep-interview"><img src="https://agentmods.dev/badge/skills/orinks/accessiweather/deep-interview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 8 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 314 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 56 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00019 | $0.06391 |
| Opus 5.5 | $0.00008 | $0.02556 |
| Sonnet 5.5 | $0.00004 | $0.01278 |
| Haiku 4.5 | $0.00002 | $0.00639 |
Grade A, and why
deep-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 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.
How it starts
The opening of the file, as written. The whole thing — 469 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Use_When>
- The request is broad, ambiguous, or missing concrete acceptance criteria
- The user says "deep interview", "interview me", "ask me everything", "don't assume", or "ouroboros"
- The user wants to avoid misaligned implementation from underspecified requirements
- You need a requirements artifact before handing off to
ralplan,autopilot,ralph, orteam</Use_When>
<Do_Not_Use_When>
- The request already has concrete file/symbol targets and clear acceptance criteria
- The user explicitly asks to skip planning/interview and execute immediately
- The user asks for lightweight brainstorming only (use
planinstead) - A complete PRD/plan already exists and execution should start </Do_Not_Use_When>
<Why_This_Exists> Execution quality is usually bottlenecked by intent clarity, not just missing implementation detail. A single expansion pass often misses why the user wants a change, where the scope should stop, which tradeoffs are unacceptable, and which decisions still require user approval. This workflow applies Socratic pressure + quantitative ambiguity scoring so orchestration modes begin with an explicit, testable, intent-aligned spec. </Why_This_Exists>
<Depth_Profiles>
- Quick (
--quick): fast pre-PRD pass; target threshold<= 0.30; max rounds 5 - Standard (
--standard, default): full requirement interview; target threshold<= 0.20; max rounds 12 - Deep (
--deep): high-rigor exploration; target threshold<= 0.15; max rounds 20 - Autoresearch (
--autoresearch): same interview rigor as Standard, but specialized for$autoresearchmission readiness and.omx/specs/artifact handoff
If no flag is provided, use Standard.
<Mode_Flags>
--autoresearch: switch the interview into autoresearch-intake mode for$autoresearchhandoff. In this mode, the interview should converge on a validator-ready research mission, write canonical artifacts under.omx/specs/, and preserve the explicitrefine furthervslaunchboundary for downstream skill intake. </Mode_Flags> </Depth_Profiles>
<Execution_Policy>
- Ask ONE question per round (never batch multiple interview rounds into one
questions[]form) - Ask about intent and boundaries before implementation detail
- Target the weakest clarity dimension each round after applying the stage-priority rules below
- Treat every answer as a claim to pressure-test before moving on: the next question should usually demand evidence or examples, expose a hidden assumption, force a tradeoff or boundary, or reframe root cause vs symptom
- Do not rotate to a new clarity dimension just for coverage when the current answer is still vague; stay on the same thread until one layer deeper, one assumption clearer, or one boundary tighter
- Before crystallizing, complete at least one explicit pressure pass that revisits an earlier answer with a deeper, assumption-focused, or tradeoff-focused follow-up
- Gather codebase facts via
explorebefore asking user about internals - When session guidance enables
USE_OMX_EXPLORE_CMD, preferomx explorefor simple read-only brownfield fact gathering; keep prompts narrow and concrete, and keep ambiguous or non-shell-only investigation on the richer normal path and fall back normally ifomx exploreis unavailable. - Always run a preflight context intake before the first interview question
- If initial context is oversized or would exceed the prompt budget, do not paste or forward the raw payload into interview prompts; request and record a prompt-safe initial-context summary first
- The oversized initial-context summary gate is blocking: wait for the concise summary before ambiguity scoring, crystallizing artifacts, or any downstream execution handoff
- The summary must preserve goals, constraints, success criteria, non-goals, decision boundaries, and references to any full source documents so downstream consumers receive a prompt-safe but faithful context
- Keep total prompt payloads within a safe budget by summarizing or trimming retained history; preserve newest/highest-signal answers and never let raw oversized context crowd out the current question
- Reduce user effort: ask only the highest-leverage unresolved question, and never ask the user for codebase facts that can be discovered directly
- For brownfield work, prefer evidence-backed confirmation questions such as "I found X in Y. Should this change follow that pattern?"
- In attached-tmux Codex CLI, deep-interview uses
omx questionas the required OMX-owned structured questioning path for every interview round - When invoking
omx questionthrough attached-tmux Bash/tool paths, preserve the leader-pane return target by prefixing the command withOMX_QUESTION_RETURN_PANE=$TMUX_PANE(or a concrete%panevalue) - If you launch
omx questionin a background terminal, immediately wait for that background terminal to finish and read its JSON answer before scoring ambiguity, asking another round, or handing off - Treat
answers[]as the primaryomx questionsuccess contract. For a single interview round, readanswers[0].answer; use legacy top-levelansweronly as a compatibility fallback when needed. - If the current runtime is outside tmux and cannot render
omx question, use the native structured question tool when available; otherwise ask exactly one concise plain-text question and wait for the answer - Re-score ambiguity after each answer and show progress transparently
- Do not hand off to execution while ambiguity remains above threshold unless user explicitly opts to proceed with warning
- Do not crystallize or hand off while
Non-goalsorDecision Boundariesremain unresolved, even if the weighted ambiguity threshold is met - Treat early exit as a safety valve, not the default success path
- Persist mode state for resume safety (
state_write/state_read) </Execution_Policy>
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 First seen · 469 lines · 19 tokens per session scan A b047982412bd
deep-interview is a skill published in the GitHub repository Orinks/AccessiWeather (24 stars, last pushed 2d ago), licensed MIT. It adds 19 tokens to every session and 6,391 once invoked, about $0.0001 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
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
component-fixtures
Use when creating or updating component fixtures for screenshot testing or their shared infrastructure, or when designing UI components to be fixture-friendly. Covers fixture file structure, theming, service setup, CSS scoping, async rendering, validation, and common pitfalls.