菜单
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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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-
Use when writing Julia numerical or scientific code, to apply always-on performance guardrails, gate an accelerated implementation as an identity against its verbatim reference over the full domain of intended use, and escalate to reproducible benchmark gating for speedup or regression claims. Works standalone by default and can optionally emit ecosystem artifacts.
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.
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.
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.
Use when a result, manuscript, derivation, or diff needs independent review. Runs clean-room multi-agent review loops across Claude/Gemini/Codex/OpenCode/Kimi with strict review-contract checks, fallback policy, and convergence gates.
| 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. |
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 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.