Borrowing it
Nothing to install: this file belongs to AHepi/DeepReason. 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/AHepi/DeepReason/main/.claude/skills/dr-ask-the-right-question/SKILL.mdgit clone --depth 1 https://github.com/AHepi/DeepReasonWrote 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/ahepi/deepreason/dr-ask-the-right-question)<a href="https://agentmods.dev/skills/ahepi/deepreason/dr-ask-the-right-question"><img src="https://agentmods.dev/badge/skills/ahepi/deepreason/dr-ask-the-right-question.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 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 46 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 94 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 | $0.00093 | $0.02448 |
| Opus 5 | $0.00046 | $0.01224 |
| Sonnet 5 | $0.00019 | $0.00490 |
| Haiku 4.5 | $0.00009 | $0.00245 |
Grade A, and why
dr-ask-the-right-question 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 8d 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ask the right question
The two workflow families tell you what to PRODUCE. This skill tells you what to ASK, of whom, in what order — the reasoning that happens between an operator message and the first phase artifact. Working here fails in two ways: asking nothing (you invent facts and build on them) or asking the operator everything (their attention is the scarcest budget in the system). Both are the same mistake — a question routed to the wrong authority.
1. The three authorities, in cost order
Every question has a cheapest competent authority. Ascend only when the cheaper one genuinely cannot answer.
| Authority | Answers questions like | Cost |
|---|---|---|
THE RECORD — log.jsonl, objects/, typed verdicts, blobs, instruments (verify_root, tools/root_sweep.py, tools/docs_verify.py, the gate) |
what happened; which value was rejected; do two instruments agree; did anything move | a command |
THE FRAMEWORK — CLAUDE.md, docs/map/ (Traps first), docs/ERRATA.md, the tranche ledgers (REQUEST/GOAL/PARKED), prior DELIVERY reconciliations |
what is frozen; what has gone wrong here before; what did the operator already rule; where does X live | a file read |
| THE OPERATOR | genuine forks the record and framework underdetermine; frozen-surface approval; taste on user-facing shape | the scarcest budget there is |
Two rules that are always in force:
- Cite the instrument with the number. Two instruments can both be
right and disagree: the root census is 45 by direct manifest load over
git ls-filesand 42 rows byroot_sweep.py, which scansexperiments/only. A number without its instrument is not a fact yet (docs/ERRATA.mdE5, E8;DR-INV-frozen-surfaces, "The root sweep"). - Model prose is never evidence. Yours included. If your answer to a record-question does not end in a command output or a file path, you have not asked the record yet.
2. Reading the operator
The operator writes tersely and expects the repo's context to carry the rest. Each row is a real committed exchange — look it up when unsure.
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.
- 8d ago First seen · 150 lines · 93 tokens per session scan A 36f2d8880fce
dr-ask-the-right-question is a skill published in the GitHub repository AHepi/DeepReason (142 stars, last pushed yesterday), licensed MIT. It adds 93 tokens to every session and 2,448 once invoked, about $0.0005 per session on Opus 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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…
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…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
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…