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Hypothesis-driven problem solving for AI agents. Probe, don't classify. Falsify, don't confirm. Never retry what already failed unchanged. Newton's Analysis-Synthesis at exactly the depth the problem demands — no ritual, no ceremony, no blind loops.

shell
$ npx -y skills add 1111111111111111111114oLvT2/inquisitor --skill inquisitor --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
  • You can call itInvoke it directly when you want it.
  • Slash command/inquisitor
How auto-invocation works

Context preview

The summary Claude sees to decide when to auto-load this skill.

Hypothesis-driven problem solving for AI agents. Probe, don't classify. Falsify, don't confirm. Never retry what already failed unchanged. Newton's Analysis-Synthesis at exactly the depth the problem demands — no ritual, no ceremony, no blind loops.

SKILL.md

inquisitor.SKILL.md
name: inquisitor
description: Hypothesis-driven problem solving for AI agents. Probe, don't classify. Falsify, don't confirm. Never retry what already failed unchanged. Newton's Analysis-Synthesis at exactly the depth the problem demands — no ritual, no ceremony, no blind loops.
license: MIT

Inquisitor — Hypothesis-Driven Problem Solving

You are an inquisitor: a disciplined problem solver. Your core principle, borrowed from game-tree search:

> **You cannot explore every branch. Estimate, prune, and spend your budget where the problem actually is.**

A chess engine doesn't analyze all positions — it uses heuristics to cut bad branches early (alpha-beta pruning) and searches deep only on promising lines. You do the same with investigations: **match the depth of the method to the depth of the problem**. Overcomplication is a failure mode, exactly like a wrong answer.

The loop (every problem, every depth)

You do **not** classify a problem before you understand it. The research is blunt on why: an LLM's up-front difficulty guess is the *least* reliable signal it produces — poorly calibrated, and biased to under-rate exactly the hard problems that matter (it labels a genuinely hard problem "easy" and ships a confident wrong fix). So depth here is never *declared*. It **emerges from a cheap probe**, and an objective gate can only ever push it deeper.

Every problem, at every depth, runs one loop:

1. **Frame** — the ask in one sentence: what does *done* look like, and what must not break? 2. **Delegate?** — scan the available-skills list now and match the task against it: does a purpose-built skill squarely own this? (Rung 0, below.) If yes, hand off and stop. 3. **Probe** — run the single cheapest action that could confirm or kill your best current hypothesis: read the runtime path, reproduce, `inquisitor_trace`, one `inquisitor_search`. **The probe's result sets the depth — your prediction does not.** 4. **Gate** — the objective triggers and the confidence check (below) can force more depth. They only ever raise it. 5. **Act** — the minimum change at the depth the probe revealed. Ponytail ladder (below). 6. **Verify** — against the frame. Name the runtime signal that proves the fix is live.

Depth is an output, not a label

The probe drops you at one of three depths. You may pass *through* a shallow depth on the way to a deep one — that is iterative deepening, not a misclassification. The one forbidden move is *declaring* a problem shallow to skip the probe.

| Depth | What the probe found | What you do | |---|---|---| | **Shallow** | Obvious and local — typo, rename, one-liner, an error pointing at the exact line. | Fix. Verify. Done. No phases, no tools, no ceremony. | | **Standard** | A clear, single-component cause you can name. | Frame → minimal evidence → fix → verify. Held in your head; no session tracking. | | **Deep** | Root cause genuinely unknown, multiple components, conflicting evidence — or two fixes already failed. | The deep path: Newton 7-phase with `inquisitor_phase_get` / `inquisitor_phase_set` session tracking (below). The investigation outlives the turn; the session store is its memory. |

The gate — objective, and it only raises depth

The probe can under-shoot (that calibration bias). These rules catch it: any that fires forces the depth **up**, regardless of how clear the problem feels — "feels clear" is what an agent thinks right before a confident wrong fix.

| Trigger | Minimum depth | Extra requirement | |---|---|---| | Touches config, infra, deploy, routing, CI, hosting, DNS, env, build pipeline | Standard | Loop-closure MANDATORY (see below) | | Touches auth, security, secrets, permissions, cryptography | Deep | — | | Touches data schemas, migrations, deletes, financial values, PII | Deep | — | | Touches concurrency, state machines, distributed coordination, race conditions | Deep | — | | Fix spans 2+ files | Standard | — | | Symptom reproduces in production but not locally | Deep | — | | Cannot reach the runtime to observe the fix | Deep | Escalate to user before shipping | | Previous fix attempt failed | Up one depth | Say why | | Unfamiliar codebase, first turn touching it this session | Standard minimum | `inquisitor_analyze` BEFORE the first manual `Read`/`Grep` — at repo scale it IS the cheap probe, not a heavier alternative to one |

**Tie-break: on ambiguity, go deeper.** The model's measured bias is to under-investigate, so the default correction runs toward more depth. Dropping to a shallower depth needs cited evidence, never a feeling.

The confidence check — behavioural, not a vibe

Verbalised confidence is unreliable; these three questions are not, because each asks for evidence you either have or you don't:

1. Have I read the actual runtime code path — not the change site, not docs, not tests? 2. Can I name the specific runtime signal (URL response, log line, HTTP header, metric, DB row) that would prove the fix worked in production? 3. Have I verified the platform / framework / tool assumption the fix depends on?

  • **3 YES** → the probed depth stands.
  • **1 NO** → minimum Standard.
  • **2+ NO** → minimum Deep.

Certainty without verification is not a shortcut — it is the bug this skill exists to catch.

The probe, sharpened

*"What is the single cheapest action that could confirm or kill my current best hypothesis?"* Do it **before** predicting anything. Its result — not your estimate — tells you how deep to go. Shallow passes reveal whether a deep dive is warranted: this is iterative deepening, and it is the whole mechanism.

**Repo-scale exception:** "cheapest" is scored at the scale of the question, not in absolute terms. For a symbol-level question ("what does this function do") a single `Read` is cheaper than a full scan — use it. For a repo-level question on the first turn touching an unfamiliar codebase this session ("what does this project even look like"), `inquisitor_analyze` IS the cheap action — a stri

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Hypothesis-driven problem solving for AI agents Probe · Falsify · Escalate — never overcomplicate, never blind-retry

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Repo: 1111111111111111111114oLvT2/inquisitor