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xray

106 Cross-Runtime Skills | 7 Claude Code Agents | One Command Install

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项目 README

来源文件:README.md

抓取于 2026年9月8日

Spellbook

106 Cross-Runtime Skills | 7 Claude Code Agents | One Command Install

A cross-runtime skill library for Claude Code, Codex, and multi-agent workflows.

Stars License Skills Agents

Quick Start • Runtime Targets • Pick a Workflow • Skills • Agents • Changelog • Release Status • Contributing • 中文


Rename notice: Spellbook was formerly Claude Arsenal. Claude Code remains a first-class target; the new name reflects the broader roadmap for Claude Code, Codex, and cross-runtime agent skills. See the migration note for details.


Quick Start

Start with one job-shaped workflow. The maintained skills CLI lets you choose the supported coding agents during installation and installs only these four skills:

npx skills add majiayu000/spellbook --skill frontend-design --skill app-ui-design --skill ui-design-system --skill figma-to-react

Use npx skills add majiayu000/spellbook --list to inspect the catalog before installing. See Pick a Workflow for four other focused starting points.

Advanced Cross-Runtime Installer

install.sh remains available when you want explicit Claude Code/Codex target paths or need to install the repository's Claude Code agents as well as skills.

# Install all skills and supported agents into both maintained runtimes
curl -fsSL https://raw.githubusercontent.com/majiayu000/spellbook/main/install.sh | bash -s -- --target all

# Or clone the repository and select skills explicitly
git clone https://github.com/majiayu000/spellbook.git
cd spellbook
./install.sh --target all --skills typescript-project,python-project,devops-excellence

Verify Installation

  • Claude Code: type / to see your installed skills.
  • Codex: restart Codex so it reloads ~/.agents/skills.

Runtime Targets

Spellbook keeps the skill source in one place and installs it into the runtime you use.

TargetInstalled ToStatus
Claude Code~/.claude/skills plus ~/.claude/agentsSkills and agents supported
Codex~/.agents/skillsSkills supported; agents skipped
AllBoth Claude Code and Codex pathsRecommended for multi-tool users

Claude Code remains a first-class target and search entry. The project was formerly known as Claude Arsenal; the new Spellbook name reflects the broader goal: reusable skills that can travel across coding agents. Older Spellbook versions installed Codex skills under ~/.codex/skills; reinstall with the current installer to use the documented Codex user-level skill path.


Pick a Workflow

Start with a small bundle that matches the job, then add more skills when the workflow sticks.

WorkflowInstallGood for
Frontend and UInpx skills add majiayu000/spellbook --skill frontend-design --skill app-ui-design --skill ui-design-system --skill figma-to-reactProduct UI, landing pages, design systems, Figma handoff
Code qualitynpx skills add majiayu000/spellbook --skill codebase-audit --skill flowguard --skill systematic-debugging --skill review-gateAudits, guarded delivery, root-cause debugging, pre-landing review
Ops and releasenpx skills add majiayu000/spellbook --skill release-engineering --skill server-security --skill clash-doctor --skill system-doctorRelease planning, server hardening, and local or network diagnosis
Product and docsnpx skills add majiayu000/spellbook --skill product-discovery --skill prd-master --skill technical-spec --skill product-analyticsDiscovery, PRDs, technical specs, metrics plans
Agent workflowsnpx skills add majiayu000/spellbook --skill codex-agent --skill multi-ai-research --skill flowguard --skill vibeguardCross-review, multi-AI research, context handoff, anti-hallucination checks

High-signal individual skills to try first: github-trending, harmonyos-app, app-ui-design, product-discovery, xiaohongshu, codebase-audit, and server-security.

See Showcase for copy-paste prompts and expected outputs. Use the Spellbook Skill Browser for curated first-party skills, or the Claude Skills Registry for broader community discovery. Release history lives in Changelog.


Why Spellbook

  • Cross-runtime install: one source tree can install into Claude Code and Codex.
  • Validated registry: every installable skill is checked by python3 scripts/validate_skills.py --check.
  • Progressive disclosure: larger skills use references/, templates/, scripts/, and eval files instead of one giant prompt.
  • Practical coverage: engineering, operations, product, UI, content, and agent workflows live in one catalog.

Skills

The generated full skill inventory lives in Skill Registry. Skill layout rules live in Skill Format Policy. Skill authoring quality rules live in Skill Quality Playbook.

Search the Registry

# Free-text query (AND semantics across name, description, category, tags)
python3 scripts/validate_skills.py search rust testing

# Filter by tag
python3 scripts/validate_skills.py search --tag agent

# Restrict to a description language
python3 scripts/validate_skills.py search --language zh deploy

# Machine-readable output
python3 scripts/validate_skills.py search --tag react --json

The tag index lives in registry/tags.json for tooling and dashboards. Curated overrides for skills the keyword heuristic cannot infer live in registry/tag_overrides.yml.

Audit non-blocking skill quality signals:

python3 scripts/audit_skill_quality.py
python3 scripts/audit_skill_quality.py skill-creator

AI & Agent Workflow

Skills for orchestrating, guarding, and maintaining AI agent workflows — the core of Spellbook's cross-runtime mission.

SkillDescription
multi-model-orchestratorCoordinate multi-agent tasks via a centralized handoff document
flowguardGuard long, ambiguous, or stateful agent tasks from drift
skill-lifeguardAdd reliable-skill contracts, checkpoints, smoke hooks, and drift signals
review-gateProduce review packs and require human approval before landing agent changes
skill-auditAudit, design, categorize, and measure agent skills
skill-ecosystem-doctorGovern canonical sources, projections, retirement, quarantine, and cross-runtime verification
threadsCodex-native subagents and parallel GitHub queue lanes
codex-fluentCodex session hygiene, archive strategy, and handoff discipline
codex-retrospectiveCodex self-review of recent history to improve behavior
brainstormingSocratic dialogue for design refinement and architecture exploration

See docs/agent-reliability-trio.md for the Reliable Skill + Context Engineering + Review Gate workflow.

Development Architecture

Build production-ready projects with language-specific best practices.

SkillLanguageKey Features
typescript-projectTypeScriptESM, Zod, Biome, Clean Architecture
python-projectPythonuv, Pydantic, Ruff, FastAPI
rust-projectRustCargo workspace, error handling, async
golang-webGoChi/Echo, sqlc, structured logging
zig-projectZigBuild system, memory management
architecture-foundationCross-languageRuntime, state ownership, adapters, and convergence specs
elegant-architectureCross-languageClean architecture with strict 200-line file limits

Product Lifecycle

End-to-end product development from discovery to deployment.

SkillPhaseWhat You Get
product-discoveryDiscoveryJTBD, user interviews, market research
prd-masterDefinitionPRD writing, user stories, RICE prioritization
technical-specDesignDesign docs, ADR, C4 diagrams
product-analyticsGrowthEvent tracking, A/B testing, AARRR
devops-excellenceDeploymentCI/CD, Docker, Kubernetes, GitOps
observability-sreOperationsMonitoring, logging, tracing, SLO/SLI
product-manager-toolkitDefinitionRICE, customer interviews, PRD templates, discovery frameworks

API & Backend

SkillDescription
api-designREST/GraphQL/gRPC patterns, OpenAPI 3.2
auth-securityOAuth 2.1, JWT, security best practices
database-patternsPostgreSQL, Redis, migrations, optimization
codebase-auditDeep adaptive repository audit with severity-ranked findings and repair roadmap
structured-logging-liteCentralized logging, field standards, and distributed tracing

Development Practices

SkillDescriptionOrigin
contributorEnd-to-end open source contribution workflow from issue discovery to PR submissionCustom
repo-agent-context-auditAudit and scaffold repo agent context across AGENTS, skills, and specsCustom
skill-creatorCreate, improve, and benchmark reusable skillsCustom
humanizerRemove obvious AI writing patterns from user-facing textExternal guide + custom adaptation

Delivery Workflow

Disciplined end-to-end delivery: testing, commits, health checks, and contribution flow.

SkillDescription
app-user-story-qaEnd-to-end app feature inventory, canonical tracker, user-story testing, fixes, and retest loop
test-driven-developmentEnforce RED-GREEN-REFACTOR TDD discipline
comprehensive-testingTest pyramid, unit/integration/E2E/property testing, framework best practices
git-commit-smartGenerate meaningful conventional commit messages from diff
push-allStage, commit, and push all changes after safety checks
project-health-auditorCodebase health, tech debt, dependency, and project risk analysis
contribution-architectMove from bug fixes to architectural improvements and debt discovery

Cross-Tool Interop

Skills for using multiple coding agents and CLI tools together.

SkillDescription
codexInvoke Codex CLI sessions from another agent workflow
codex-agentOptional second-opinion review, cross-verification, and alternatives through Codex CLI
sol-luna-routerKeep GPT-5.6 Sol as commander/reviewer while GPT-5.6 Luna performs bounded implementation
ask-opencliAsk Grok or Gemini through opencli and an existing browser session
multi-ai-researchParallel research across multiple AI tools and internal agents

UI/UX & Design

SkillDescription
app-ui-designiOS/Android UI design, Material Design 3, HIG
product-ux-expertUX evaluation, heuristics, accessibility
frontend-designWeb frontend design patterns
ui-designerExtract design systems from UI screenshots and references
ui-design-systemDesign system toolkit and design-dev handoff support
web-artifacts-builderClaude.ai HTML artifacts
react-best-practicesReact and Next.js performance patterns distilled from Vercel guidance
react-hooks-best-practicesReact hooks, effects, refs, and component design patterns
slidesSpeech-friendly slide deck and background slide generation
ui-ux-pro-maxCompact UI/UX tables for product patterns, landing pages, charts, and 9 stacks
figma-to-codeFigma designs to production React/Next.js with TypeScript and Tailwind
css-debugDiagnose CSS/layout issues, Tailwind conflicts, z-index stacking
playwright-automationBrowser automation and testing with Playwright

Tooling & Automation

SkillDescription
web-asset-generatorFavicons, app icons, OG images
github-trendingGitHub trending analysis
vibeguardTask contracts, finding scoring, and lightweight anti-hallucination reviews
clash-doctorClash proxy & network diagnostics
clash-routesInspect active proxy routes for specific processes via Mihomo API
optimize-networkSafe local network speed, latency, DNS, Wi-Fi, and bufferbloat diagnostics with VPN/proxy guardrails
disk-cleanerScan and reclaim disk space with interactive cleanup guidance
system-doctorDiagnose CPU, memory, and process-level system slowdowns
codex-log-guardDiagnose and mitigate excessive Codex local SQLite diagnostic log writes
server-securityAudit and harden Linux server SSH, firewall, and exposed services
cliproxy-newapi-stackAdd a loopback-first NewAPI metering layer to an independently verified CLIProxyAPI upstream

Operations & Deploy

Deploy models and diagnose local and remote environments.

SkillDescription
gemma4-local-deployDeploy Gemma 4 12B locally on Mac/Apple Silicon via llama.cpp or Ollama
gpu-useInspect remote server GPU usage (per-card VRAM, processes, containers)
rustdesk-doctorDiagnose RustDesk connection issues
vscode-doctorDiagnose slow or freezing VS Code-compatible editors

Content & Social Media

SkillDescription
xiaohongshuXiaohongshu content creation & publishing
trip-plannerTravel itinerary planning
weeklyWeekly report from Git, Claude Code, and Codex sessions
xiaohongshu-netfeel-guardianRemove translation-tone from Claude's Chinese content for native readability

Mobile & Cross-Platform

SkillDescription
harmonyos-appHarmonyOS with ArkTS, ArkUI, Stage Model

Rust Specific

SkillDescription
rust-best-practicesMicrosoft Rust guidelines, error handling

Agents

Specialized agents for complex tasks.

AgentExpertiseUse Case
tech-lead-orchestratorCoordinationMulti-step tasks, delegation
code-archaeologistExplorationLegacy codebase documentation
backend-typescript-architectArchitectureBun/Node.js, API design
senior-code-reviewerReviewSecurity, performance, architecture
kubernetes-specialistInfrastructureK8s, Helm, GitOps
security-auditorSecurityOWASP Top 10, SAST
opensource-contributorContributionOpen source workflow

Plugins

Spellbook is also a Claude Code plugin marketplace. Install the repo as a marketplace, then install plugins from it:

/plugin marketplace add majiayu000/spellbook
/plugin install idea-coach
/plugin install rust-dev
PluginDescription
idea-coachOpinionated product coach (idea -> PRD -> clickable HTML prototype) + multi-role idea group chat; plugin commands are /idea-coach:idea and /idea-coach:idea-team
rust-devRust best practices, code review, performance, and async patterns

Plugin skills are packaged copies of catalog skills; the catalog (installed by install.sh) remains the cross-runtime source of truth.


Skill Design Philosophy

Every skill in Spellbook follows these principles:

  1. Hard Rules - Mandatory constraints with FORBIDDEN / REQUIRED markers
  2. Practical Examples - Real code, not just theory
  3. Verification Checklists - Actionable validation steps
  4. Battle-Tested - Used in production environments

Documentation

DocumentDescription
ChangelogRelease history and current release status
Installation GuideDetailed setup instructions
Runtime TargetsClaude Code and Codex installation targets
ShowcaseCopy-paste workflow demos
Spellbook Operating ContractAgent behavior rules for autonomy, escalation, pushback, feedback loops, and done-when checks
Skill Format PolicyDirectory vs file skill layout rules
Skill Quality PlaybookTrigger descriptions, gotchas, progressive disclosure, and verification
Skill Testing GuideHow to validate skills work
Creating PluginsBuild your own skills
Product Lifecycle (EN)Full lifecycle coverage
Product Lifecycle (中文)产品生命周期覆盖

Release Status

Spellbook is in pre-1.0 release-readiness mode. No numbered GitHub release tag has been cut yet; the current install path uses the repository main branch. See Changelog for release history.

Current limitations:

  • Codex installs skills only; Claude Code agents are skipped for Codex targets.
  • Some skills depend on external CLIs, accounts, credentials, or platform access that are not bundled by the installer.
  • The registry validator checks installable skill structure, not every external workflow end to end.

Support paths:


Credits

Built on the shoulders of giants:


Contributing

Contributions welcome! Please read our Contributing Guide first.


The Agent Infra Stack

This project is one layer of an open-source stack for running coding agents (Claude Code, Codex) as serious infrastructure. Every piece works standalone; together they close the loop:

spellbook sits in the Extend layer — the authoring side of the skill story: write once, run on Claude Code and Codex. Discovery and distribution live in claude-skill-registry.

LayerProjectWhat it does
Extendclaude-skill-registryDiscover and search community Claude Code skills
Extendspellbook ◀ you are hereCross-runtime skills for Claude Code, Codex, and multi-agent workflows
TrustargusStatic install-time scanner for supply-chain attacks (npm / PyPI / crates.io)
TrustvibeguardRules, hooks, and guards against hallucinated or unverified agent changes
RememberrememLocal-first persistent memory for Claude Code and Codex sessions
OrchestrateharnessRust agent orchestration platform — rules, skills, GC, observability
Routelitellm-rsHigh-performance Rust AI gateway — 100+ LLM APIs via OpenAI format
KeepkeeplineSession command center — monitor, recover, never lose agent work

License

MIT License - Use freely in your projects.


If this helps you, consider giving it a ⭐

Made for builders using Claude Code, Codex, and multi-agent workflows

开发与工程文档与办公研究与检索

高风险

  • 来源需自行核对维护者身份。
  • 包含脚本或命令调用,安装前请复核。
  • 可能需要外部 token、网络权限或第三方服务。
  • 存在潜在风险命令,请谨慎安装。
  • 扫描发现:2 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/majiayu000/spellbook.git
  3. 将 "skills/xray" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/majiayu000/spellbook.git
  3. 将 "skills/xray" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/majiayu000/spellbook.git
  3. 将 "skills/xray" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/majiayu000/spellbook.git
  3. 将 "skills/xray" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/majiayu000/spellbook.git
  3. 将 "skills/xray" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: xray
description: Investigate how a concept, code path, application, network flow, system, incident, document, or local artifact actually works, then deliver a two-depth visual HTML explainer with a dead-simple big-picture first layer and source-backed technical depth behind it. Use when the user invokes $xray, asks how something works under the hood, wants a module or app behavior traced across boundaries, requests web, code, network, or safe static artifact research before explanation, or asks for an evidence-backed explainer. Do not use for answer-only definitions, general web design, full codebase audits, or invasive binary reverse engineering without explicit authorization.

X-Ray Explainer

Investigate first and explain second. The HTML is a projection of verified understanding, not decoration around an early guess.

Operating Boundary

  • Directly perform read-only research, repository inspection, log/config examination, source archaeology, and creation of the requested explainer artifact.
  • When the user asks to analyze a clearly identified app, CLI, or local compiled artifact they are entitled to inspect, include safe read-only static inspection when it can answer the teaching question. Record identity and hash first; inspect metadata, signatures, dependencies, imports, recoverable symbols, strings, entitlements, and bundled resources without modifying or executing the target.
  • For an authorized native binary or compiled CLI, load $claude-code-reverse first and use its tested extract.sh workflow as the canonical identity, hash, cache, and safe static baseline. Do not duplicate that baseline inside X-Ray.
  • When the canonical static baseline cannot establish the requested mechanism, or the authorized target is an APK, JavaScript bundle, or protocol flow, read reverse-core.md. Load only the matching specialist adapter, use tools already available in the environment, and return its evidence to the ordinary X-Ray causal model. Reverse Core is an internal depth route, not a second user-facing skill or a reason to install a full security pack.
  • Ask before executing an unknown binary, attaching a debugger, intercepting or decrypting traffic, patching an artifact, installing reverse-engineering tools, using a paid endpoint, touching production, accessing credentials, or changing product code.
  • Never bypass access controls, fabricate evidence, or treat agreement between models as corroboration.
  • If the target, revision, authorization, or intended audience would materially change the investigation, clarify that one fact before acting.

Investigate

Read research-routing.md, then choose the narrowest route that can answer the question.

TargetDefault route
Stable concept with adequate supplied materialExplain from supplied evidence; verify pivotal facts only
Current, niche, disputed, or unfamiliar topicSearch the web; prefer primary and authoritative sources
Repository, module, app behavior, API path, or architectureTrace the reachable path across code, network, persistence, and background work
Incident or wrong runtime behaviorInspect persisted state, logs, metrics, running revision, then code
Clearly identified local app, CLI, or compiled artifactUse $claude-code-reverse for the native static baseline, then load one Reverse Core specialist adapter only if the question remains unresolved

Do not invoke extra agents or external AI systems by default. Add them only when the user requests delegation or a separate workflow explicitly requires it.

Announce each stage in one line as it begins: the stage name, what is being checked, and the next visible output. A long investigation must never go silent while the user waits.

1. Frame the teaching question

Write one sentence naming what the reader must understand or decide after viewing the explainer. Default to a curious adult who is new to the topic; never infantilize the reader.

2. Acquire ground truth

Read evidence-contract.md. For code or runtime targets, also read code-archaeology.md.

  • Establish the exact target and relevant version before explaining it.
  • For a compiled target, record the artifact path, cryptographic hash, architecture, and signature before drawing conclusions. Treat strings, imports, symbols, and decompiled fragments as clues until another observation establishes their role.
  • For authorized specialist reverse work, keep the reverse phase bounded to the teaching question. Prefer one reachable path over exhaustive decompilation, and bring exact addresses, symbols, tool versions, and uncertainty back into the same evidence model.
  • Search the local target before searching the web for explanations of it.
  • Use web research for current, niche, disputed, unfamiliar, or explicitly source-backed claims.
  • For incidents, prefer the actual persisted state and running revision over remembered browser behavior or design intent.
  • For application behavior, follow the user action through internal control flow, network boundaries, server handling, persistence or background work, and the result or failure path. Inspect or capture traffic only when it is safe, authorized, and materially useful.
  • Keep exact URLs, code anchors, revision identifiers, timestamps, and uncertainty for the claims that matter to the explanation.

3. Form the causal model

  • Write scratch notes in whatever form helps distinguish evidence from interpretation; do not create a manifest or fixed ledger unless the task genuinely benefits from one.
  • Identify the input, meaningful transformations or decisions, state changes, outputs, and failure boundaries that explain the behavior. Use as many or as few steps as the mechanism needs.
  • State confirmed behavior plainly. Label material inference and unknowns where a reader could otherwise mistake them for fact; do not badge every sentence mechanically.
  • Preserve technical truths that affect behavior. Simplify vocabulary, not causality.
  • Introduce a real mechanism before using an analogy. Label where the analogy stops matching reality.
  • Keep unresolved contradictions and missing evidence visible.

4. Design the visual story

Read visual-explanation.md. Choose the diagram from the causal structure: flow, sequence, state machine, architecture, timeline, comparison, or a small simulator.

When the investigation produces meaningful technical evidence, project the same causal model at two depths:

  • The orientation layer comes first. Give the shortest accurate answer and one dominant, plain-language visual that a newcomer can understand without reading the evidence layer. Lead with how the subject works, not what the researcher inspected.
  • The evidence layer follows or expands on demand. Preserve code symbols, network branches, persistence, failure behavior, sources, and material unknowns for readers who want to verify or continue digging.

Keep the first layer visually quiet and low in terminology. Move implementation detail down instead of deleting it. Do not impose a fixed word count, step count, card count, or DOM structure; use the smallest first layer that carries the real mechanism. A genuinely simple topic does not need a padded second layer.

5. Write for a person

  • For a Chinese explainer, read and apply chinese-writing.md after the evidence and causal model are stable. Use this built-in writing pass to revise headings, body copy, captions, and the handoff so the prose sounds like a knowledgeable person walking the reader through what they found.
  • Keep technical literals, code symbols, versions, direct quotations, uncertainty, and citation meaning unchanged during the prose pass. When naturalness and precision conflict, preserve precision and rewrite the surrounding sentence.
  • Let concrete observations carry the explanation. Remove report-like labels, repetitive summaries, symmetrical card copy, fake suspense, and generic insight phrases.
  • For other languages, match the same audience-aware standard without forcing Chinese writing rules onto the text.

6. Render the artifact

  • Produce one self-contained HTML file with inline CSS and SVG or canvas. Avoid remote fonts, scripts, images, and stylesheets; ordinary source links are allowed.
  • Create the artifact in a temporary task directory by default. Put it in a project only when the user requests a durable project artifact.
  • Organize the page around the teaching question. Do not force fixed sections, card counts, or diagram shapes.
  • State the teaching question in the reader's language at the top of the page. When the page uses evidence markers, put a short legend at their first use so a first-time reader can decode Observed, Corroborated, Inferred, and Unknown without leaving the page.
  • Keep the orientation layer visible before technical inventory, provenance, or methodology. Use progressive disclosure when the evidence layer would otherwise compete with the main explanation.
  • Keep source markers adjacent to the claim or diagram step they support.
  • Use JavaScript only when interaction materially teaches the mechanism.

7. Verify before delivery

Open or render the page at a desktop and narrow viewport when a renderer is available. When no interactive renderer is at hand, scripts/render-check.sh uses an installed Playwright CLI to capture the complete page at both widths in a fresh output directory. Re-check every full-bleed or negative-margin block at the narrow width; it is the classic source of silent horizontal overflow. Inspect clipping, overflow, legibility, unresolved placeholders, source-link behavior, and whether the visual sequence still makes sense without narration. Exercise any interaction that carries explanatory meaning. If no renderer is available at all, report visual verification as incomplete instead of implying it passed.

Done When

  • Pivotal claims are traceable to current evidence or explicitly labeled inference/unknown.
  • Repository explanations include exact paths and symbols; web explanations include direct source URLs.
  • Specialist reverse explanations identify the exact artifact and tool, preserve address or symbol anchors, distinguish static clues from reachable behavior, and disclose any action that crossed the static-analysis boundary.
  • The mechanism is simpler than the source material without losing a behavior-changing fact.
  • A reader can understand and remember the central mechanism from the orientation layer alone, while the evidence layer still supports the technical claims.
  • Chinese prose has received the built-in Chinese writing pass without changing evidence or technical meaning.
  • The page has been checked in proportion to its complexity; when possible, it has been visually inspected at wide and narrow widths.
  • The page opens with its teaching question and, when it uses evidence markers, carries a short legend a first-time reader can decode.
  • The final response links the artifact and briefly states sources, uncertainty, and any boundary that prevented deeper investigation.

Gotchas

  • “Few words” does not mean “few facts.” Remove repetition before removing causal steps.
  • Deep research does not earn the first screen. Packaging, methodology, provider matrices, hashes, and source inventories belong below the central mechanism unless one of them is the teaching question.
  • A beautiful diagram of an unverified mechanism is still wrong.
  • Safe static inspection of an identified local artifact belongs in ordinary X-Ray investigation. Debugging, interception, patching, protection bypass, credential access, and execution of an unknown artifact are separate authorization levels.
  • Multiple model answers are leads, not independent sources.
  • Local code and runtime evidence outrank generic web explanations of a similarly named system.
  • Do not turn the investigation into an exhaustive audit. Stop when further research would add detail without changing the causal model.
  • Do not dump a reverse-engineering tool inventory into the explainer. Load one matching adapter, collect the evidence that changes the causal model, then return to X-Ray.
  • Do not turn quality guidance into a hardcoded content validator. Use judgment for semantic quality and ordinary rendering or syntax checks for mechanical defects.
  • Do not let a prose rewrite strengthen a claim, erase a limitation, or detach a citation from the fact it supports.
  • Avoid decorative dashboards, excessive cards, and meaningless animation. Every visual element must teach a relationship.
  • When evidence is insufficient, explain what is known, what is unknown, and the next cheapest observation that would resolve it.

Drift and Feedback

Representative prompts live in evals/evals.json. When a run produces invented anchors, missing citations, shallow research, text walls, or a misleading visual structure, patch the smallest responsible instruction, reference, template, or eval case rather than expanding the main instructions indiscriminately.

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