map

A planning guide for uncertain coding tasks. It maps what is known, what still needs checking, and which direction to take before code is written.

In plain words
What is it for?
Use it to explore unfamiliar codebases, compare possible designs, or adapt a reference implementation when the desired result is not yet clear.
Why use it?
It prevents building the wrong thing based on an untested assumption. It turns expensive rewrites into smaller decisions.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/megaprompting/torque-loop/map
Any agent
npx skills add Megaprompting/torque-loop --skill map
Clone the repo
git clone --depth 1 https://github.com/Megaprompting/torque-loop

Made for: Claude Code, Codex.

Per session 163 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,489 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00163 $0.02489
Opus 5 $0.00081 $0.01244
Sonnet 5 $0.00033 $0.00498
Haiku 4.5 $0.00016 $0.00249

Measured yesterday against content hash fcd5f019f0a4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

map 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 yesterday.

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.

skills/map/SKILL.md · 188 lines

How it starts

The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/ratchet:map — the fog-of-war gate

When uncertainty is high, the expensive mistake is a confident build in the wrong direction. /ratchet:lock says: infer the missing value, name the assumption, move. That is correct for low-uncertainty work. This command is its counterweight for the high-uncertainty case — it maps the terrain before the build, so a wrong assumption is a one-line correction on paper instead of a rewrite three PRs deep. What you deliver is the map, not a build. You do not write code until it is handed over.

Method grafted from the explore-unknowns skill; expressed in ratchet's own vocabulary — general mechanism, own words, backed by ratchet state. (Same graft discipline as the aperture dial.)

Two moves make the walk work:

  • Show, don't ask. Never make the user describe intent from a blank page. Put something concrete in front of them — a sample, a throwaway mock, a few competing directions — and let them point at it. Recognition is cheap; invention is expensive.
  • Pre-draft their reply. Close each turn with lettered options answerable in a few characters, so the user reacts instead of composing.

Step 0 — Load state, then scan the terrain

ratchet status
ratchet snapshot repo

Scan first. Gather what already exists, what is half-built, and what was tried and reverted — before you open your mouth. Serial questions on unread terrain waste the user's attention.

Procedure

Walk the four quadrants in order, naming the current one. Disclose material findings the moment you hit them; never close a quadrant off-screen.

  1. Known knowns — settle the ground. State the facts, each cited to file:line. Mark every assumption separately and say you will treat it as true until corrected, so a wrong premise gets caught now instead of after the build.

  2. Known unknowns — one question at a time. Ask the single highest-blast-radius question first (the answer that reshapes the most), not a wall of them. Give lettered options with a recommended answer. Close each question exactly one way, in front of the user: user answer, territory (you researched it, then show question + finding), probe (only touching the repo can answer it — commission a probe card, below), or OPEN (deferred, with what would unblock it and the route that will close it: ask-user, probe, park with owner+reason, promote to an assumption with a kill test, or a defect).

Read the full file on GitHub · 188 lines

Changes

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.

  1. yesterday First seen · 188 lines · 163 tokens per session scan A fcd5f019f0a4

Subscribe to this mod's changes

map is a skill published in the GitHub repository Megaprompting/torque-loop (5 stars, last pushed 1mo ago), licensed MIT. It adds 163 tokens to every session and 2,489 once invoked, about $0.0008 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 2026-08-31.

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