Agent skill

Qec Construct

by Muuuun in Muuuun/luxas

Verifier-in-the-loop CONSTRUCTION of quantum error-correcting codes with transversal non-Clifford gates (CCZ/T).

MITAuto-check passed

Install Qec Construct

skills CLI
$ npx skills add Muuuun/luxas --skill qec-construct -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Muuuun/luxas qec-construct --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Muuuun/luxas.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/qec-construct .claude/skills/qec-construct && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
qec-construct
GitHub stars
1.2k
Token cost
~1.6k tokens
SKILL.md length
736 words
Files
9 (incl. scripts)
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Verifier-in-the-loop CONSTRUCTION of quantum error-correcting codes with transversal non-Clifford gates (CCZ/T).

  • Works in 2 steps: Read this file BEFORE writing… → Every code-construction/code-search…
  • SKILL.md covers Routing (brain: read this…, The one rule: CONSTRUCT, don't…, The verifier: scripts/qverify… and The loop (this is the whole…, plus 3 more sections
  • Runs Python scripts from its folder

What it does

Qec Construct is an agent skill from Muuuun/luxas. Verifier-in-the-loop CONSTRUCTION of quantum error-correcting codes with transversal non-Clifford gates (CCZ/T). Applies whenever the project goal is a new or better code/construction — INCLUDING search-phrased goals ("find codes beating X"), where the construct-loop (propose algebraic rule → qverify → debug) is the REQUIRED mode, replacing blind random/grid sampling (empirically caps far below frontier). Gate certification is currently abelian/CCZ only, validated against the 11 Menon codes; CSS + distance checks…

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts (for example `scripts/kernel/ccz_subrank.py`, `scripts/kernel/cheap_screen.py` and `scripts/kernel/group.py`).

The repository describes itself as: An autonomous research colleague — from a question to a compiled manuscript, while you sleep. The licence is MIT.

Example prompts

  • “find codes beating X”
  • “/qec-construct”

Requirements

  • Python 3

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Read this file BEFORE writing notes/plan.md.
  2. Every code-construction/code-search experiment spawn task MUST (a) include the

What it can do on your machine

Read from SKILL.md and the folder at commit 9f77cef. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 7 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Qec Construct loads about 1.6k tokens when it runs. Until then it costs about 173 tokens; SKILL.md has 736 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~173
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from Muuuun/luxas at commit 9f77cef, republished under its MIT licence (© Muuuun). 736 words, ~1,579 tokens.

Download SKILL.mdSave it as .claude/skills/qec-construct/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
qec-construct
description
Verifier-in-the-loop CONSTRUCTION of quantum error-correcting codes with transversal non-Clifford gates (CCZ/T). Applies whenever the project goal is a new or better code/construction — INCLUDING search-phrased goals ("find codes beating X"), where the construct-loop (propose algebraic rule → qverify → debug) is the REQUIRED mode, replacing blind random/grid sampling (empirically caps far below frontier). Gate certification is currently abelian/CCZ only, validated against the 11 Menon codes; CSS + distance checks are general. Brain, do not run this yourself — forward this SKILL.md path into every code-construction experiment spawn task; experiment/tool_impl run the loop.

qec-construct — a QEC verifier-in-the-loop

Routing (brain: read this section, forward the rest)

Applies when RESEARCH.md's goal is a new or improved QEC code / code family / transversal-gate construction — construct, invent, design, discover, improve distance/FOM, and search-phrased variants ("find codes beating X") equally.

Does NOT apply to magic-state cultivation, lattice surgery, decoder, or imaging projects: the gate check is abelian/CCZ-only and those are out of scope regardless of wiring.

Brain's only two obligations (do not run the loop yourself):

  1. Read this file BEFORE writing notes/plan.md.
  2. Every code-construction/code-search experiment spawn task MUST (a) include the literal path skills/qec-construct/SKILL.md with the instruction to read it in the Design phase and wrap scripts/qverify as a tool, and (b) frame the experiment as a construct-loop (propose a parametrized ALGEBRAIC construction → qverify → debug the failure), never as random/grid sampling over supports — sampling is the mode that capped at FOM ~10.4 vs frontier 45.5.

Broad/random/hill-climb search over code supports is a known dead end for this problem (empirically caps far below the frontier; the good codes are rare isolated optima). Your job is to propose a parametrized ALGEBRAIC construction — a group + a generating rule for the supports + (for a new gate) the cup-product / Leibniz conditions — and debug it against the sound verifier, not to sample points and hope.

If you find yourself enumerating random supports, stop: that is the wrong mode.

The verifier: scripts/qverify (this is your Lean)

Call it at high frequency. Input a construction spec (JSON), get a SOUND verdict — including which condition failed, so you can fix it.

echo '{"family":"abelian","group_shape":[3,4,5],
       "supp_a":[[0,1,3],[0,3,0],[2,1,3],[2,3,1]],
       "supp_b":[[1,1,4],[1,2,2],[2,1,1],[2,2,4]],
       "supp_c":[[0,0,4],[2,0,1]],
       "frontier_fom":14.4}' | scripts/qverify

What it checks, cheapest first:

  • CSS validity (H_X H_Z^T = 0), n, k — exact GF(2), instant. Fails loudly if your construction isn't even a code.
  • Cheap sound screen — a fast heuristic upper bound UB on d_Z (~sub-second, ~100x faster than exact). Reports FOM_upper = k*UB^3/n. If FOM_upper < frontier the code provably can't beat it → reject (never rejects a real winner, since UB ≥ d_Z). Otherwise → promote.
  • Gate (CCZ) check (supply partition_a/b/c) — STCP/Leibniz conditions hold? logical CCZ non-trivial? K_CCZ (extractable gates). Abelian path is validated against all 11 published Menon codes.
  • "exact":true — sound exact distance via ILP-to-optimality. Only this certifies distance. Run it only on promoted candidates.

The loop (this is the whole method)

  1. Propose a construction (group + support-generating rule + preorientation).
  2. qverify it (cheap). If css or gate fails → read which condition failed → fix the construction (this is the Menon-style derivation-debug; e.g. the naive cup-product conditions force d=2 — find the offset/structure that escapes it).
  3. Keep the cheap screen as your fitness: iterate the construction rule to push FOM_upper up while keeping the gate valid.
  4. On a promoted candidate, run "exact":true to certify.
Show full SKILL.md (293 more words)Show less

The soundness gate — DO NOT violate

A code counts as a new frontier code ONLY if a single qverify run shows: css_valid ✓, gate.preserves_codespace/stcp_conditions_hold ✓, K_CCZ ≥ 1, and distance_exact.status == "optimal" with fom_exact strictly above the frontier. Estimated / biased-sampled / timed-out distances never certify a win. If you report a win on anything less, it is a hallucination, not a result.

Where the new codes likely are

The abelian families are heavily optimized (you will mostly re-find Menon). The upside is the under-explored non-abelian region ("family":"general", "group":{"kind":"dihedral","n":..} or {"kind":"perm","generators":[..]}). The CSS + distance + cheap-screen verifiers ARE general and sound for non-abelian. But two honest caveats: (1) the non-abelian CCZ gate check is NOT yet implemented in qverify — so a non-abelian code is CSS/distance-verified but cannot be gate-CERTIFIED here yet; implementing a group-agnostic direct-circuit coboundary-invariance check (validated against verify_stcp before ship) is the open next step. (2) Non-abelian is a high-risk region, not virgin territory: Tiew2026 already derived non-abelian weight-4 cup-product gate conditions, yet experts (Tiew, Menon) converged on abelian for deep reasons — the cup product is graded-commutative (abelian-aligned), and abelian gives Fourier/character analysis, analytic distance bounds, and hardware locality. Betting on non-abelian bets that their choice was driven by "harder to analyze" (which this skill's verifiers bypass) rather than "structurally worse" (which they don't).

Honest boundary

This skill gives you a sound, cheap, debuggable verifier and the construct-don't- search discipline. It does not guarantee a better code — proposing the right construction is still the hard part. But it is the machine that lets a construction loop actually converge instead of hallucinating, and it attacks the two real walls: no cheap classical proxy exists for quantum distance (so the screen measures the quantum quantity directly, cheaply, soundly), and the gate-validity that used to be guessed is now checked exactly.

© Muuuun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts) in skills/qec-construct of Muuuun/luxas.

  • SKILL.md
  • scripts/kernel/ccz_subrank.py
  • scripts/kernel/cheap_screen.py
  • scripts/kernel/group.py
  • scripts/kernel/tricycle_code.py
  • scripts/kernel/tricycle_distance.py
  • scripts/kernel/tricycle_stcp.py
  • scripts/qverify
  • tests/smoke_test.py

Open the folder on GitHubat commit 9f77cef

Compare with similar skills

Qec Construct next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Qec Construct compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Qec Construct this skillMuuuun/luxas1.2k—~1.6kAutomated safety check: PassMIT
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Code Reviewdotnet/maui23k—~8.2kAutomated safety check: PassMIT
Code Reviewflutter/flutter179k—~1.4kAutomated safety check: PassBSD-3-Clause
Minimal Code DisciplineYeachan-Heo/oh-my-claudecode40k—~746Automated safety check: PassMIT
Simplify CodeNousResearch/hermes-agent252k—~3.7kAutomated safety check: PassMIT

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Questions about Qec Construct

What does Qec Construct do?

Verifier-in-the-loop CONSTRUCTION of quantum error-correcting codes with transversal non-Clifford gates (CCZ/T). Qec Construct is an agent skill from Muuuun/luxas. Verifier-in-the-loop CONSTRUCTION of quantum error-correcting codes with transversal non-Clifford gates (CCZ/T).

How do I install Qec Construct in Claude Code?

Run `npx skills add Muuuun/luxas --skill qec-construct -a claude-code`. Or copy the skill folder (skills/qec-construct in Muuuun/luxas) into .claude/skills/qec-construct in your project. Claude Code loads it when a task matches its description.

How do I install Qec Construct in Codex?

Run `npx skills add Muuuun/luxas --skill qec-construct -a codex`. Or copy the skill folder (skills/qec-construct in Muuuun/luxas) into .agents/skills/qec-construct in your project. Codex loads it when a task matches its description.

Can I use Qec Construct in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Muuuun/luxas --skill qec-construct -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qec-construct, .gemini/skills/qec-construct, .github/skills/qec-construct and .opencode/skills/qec-construct in your project.

What does Qec Construct need to run?

Going by SKILL.md and its folder, Qec Construct needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Qec Construct access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Qec Construct safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Qec Construct use?

Qec Construct is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qec Construct use?

About 1.6k tokens (SKILL.md is roughly 6.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Qec Construct?

Skills that share tags, products or a category with Qec Construct: Verify (asgeirtj/system_prompts_leaks, 69k stars), Code Review (dotnet/maui, 23k stars), Code Review (flutter/flutter, 179k stars) and Minimal Code Discipline (Yeachan-Heo/oh-my-claudecode, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qec Construct?

Muuuun (a GitHub user) maintains it in Muuuun/luxas, which has 1,169 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on September 6, 2026.

Source: Muuuun/luxas on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.