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Ejecuta cualquier Skill en Manus con un clic

nullius-market

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Actualizado10 de julio de 2026 a las 17:02

Browse and install Nullius skills. This directory is the skills-market catalog (package metadata + installers), not a runnable task skill; use it to discover available skills and install specific ones into your skills home.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

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Fuente

fkguo

fkguo/nullius

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SKILL.md

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name	nullius-market
description	Browse and install Nullius skills. This directory is the skills-market catalog (package metadata + installers), not a runnable task skill; use it to discover available skills and install specific ones into your skills home.

Nullius Skills Market

This is the catalog/installer control plane for Nullius skills — it is intentionally a metadata + installer package, not a task skill. A host that linked the whole market root here (via scripts/install_codex.sh / install_opencode.sh / install_claude_code.sh / install_kimi_code.sh) sees this SKILL.md so the directory is a well-formed, inert catalog entry rather than a SKILL.md-less directory that a strict skill loader could reject.

This cross-host copy-installer is currently dormant: real distribution runs through direct in-repo symlinks today, and this catalog exists to serve hosts without a local monorepo checkout once that route goes live. See README.md for the full status.

Install specific skills (recommended)

Install only what you need (skill-pack dependencies are pulled in automatically):

python3 scripts/install_skill.py --platform <codex|claude_code|kimi_code|opencode> --package <skill-id>

List available skill ids in packages/index.json. To install every market skill as a live, repo-tracking symlink, use the per-skill installer instead of treating this catalog as a single skill:

bash scripts/install_symlink_<platform>.sh --skills-root /path/to/monorepo

This catalog entry runs nothing on its own.

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Correctness and legibility checklist for data/results figures in any field: charts, parameter scans, spectra, distributions, fit comparisons, constraint contours, heatmaps, and low-dimensional projection scatters. Use when an agent plots computed or measured data, revises a results figure for a manuscript, or audits whether a data figure is publication-ready. Covers data fidelity (excluded rows never enter plotted summaries, connected-series evaluator fingerprints are homogeneous, a rendered continuous field is continuity-scanned before delivery, claim-titles true against every axis category, one canonical value per claim), label economy floor and ceiling, colour threading with a CVD-safe palette, role-mapped typography, chart choice by data shape, a render-then-verify QA loop (bbox overlap check plus per-panel perceptual crops), a figure-reproduction provenance bundle, and a fail-closed display-acceptance gate that refuses durable or outward-facing use until every plotted quantity is bound to a verification-

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Convergence and reliability gate for NUMERICAL results in any field, including fits, optimizations, integrals, eigenvalues, roots, poles, zeros, ODE/PDE solutions, Monte-Carlo estimates, and downstream feature extraction. Use before trusting, comparing, publishing, or folding a computed number into durable research artifacts. Requires resolution convergence, independent-method checks, regression anchors, post-correction downstream recomputation, method-precondition checks, configuration-threading audits, gate-discrimination (negative-control) audits of purpose-built validation chains, accelerated/heuristic fast-path scoping (no false guarantee, unconditional escape hatch, production-validated precondition), and honest uncertainty/reporting. Emits an auditable reliability matrix. Sibling to derivation-verify for symbolic claims and julia-perf for speed claims.

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research-integrity

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AI failure-mode checklist (M1-M8) for research agents. Generic across domains. Walk M1-M7 at the moments work becomes durable — you just finished a derivation or computation and are about to write conclusions, fold a number or claim into the plan or contract, check off a task-board item, make a milestone closeout commit, hand off, assemble submission material, or claim a result is final — and, in projects using the engine's approval flow, before requesting an A1-A5 approval gate. Walk M8 before starting each heavy or production computation or hand-building a reusable computational method.

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Use when a research milestone (theory or computation) needs executed, reproducible work whose results are independently reviewed before being trusted. Milestone-based research-team workflow for theory+computation projects with reproducible artifacts, independent parallel workstreams (default: host-native subagents; configurable), and a strict convergence gate.

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review-swarm

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name	nullius-market
description	Browse and install Nullius skills. This directory is the skills-market catalog (package metadata + installers), not a runnable task skill; use it to discover available skills and install specific ones into your skills home.

Nullius Skills Market

Install specific skills (recommended)

Install only what you need (skill-pack dependencies are pulled in automatically):

python3 scripts/install_skill.py --platform <codex|claude_code|kimi_code|opencode> --package <skill-id>

List available skill ids in packages/index.json. To install every market skill as a live, repo-tracking symlink, use the per-skill installer instead of treating this catalog as a single skill:

bash scripts/install_symlink_<platform>.sh --skills-root /path/to/monorepo

This catalog entry runs nothing on its own.