Rsdoctor Analysis logo

Rsdoctor Analysis

Organization
rstackjs
rsdoctor-analysis

Use when analyzing Rspack/Webpack bundles from local `rsdoctor-data.json` and producing evidence-based optimization recommendations.

Overview

Publisherrstackjs
Repositoryagent-skills
Skill namersdoctor-analysis
Stars
93
Forks
4
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rstackjs on GitHub. Read the source before you install it.

Installation

Install the Rsdoctor Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rstackjs/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/rsdoctor-analysis .claude/skills/rsdoctor-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rsdoctor Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Rsdoctor Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Rsdoctor Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Rsdoctor analysis assistant skill

Use the globally installed rsdoctor-agent CLI from @rsdoctor/agent-cli only after a real rsdoctor-data.json path exists. Keep analysis read-only unless the user explicitly asks for install/config setup.

Response order (required): High-Priority Issues -> Proposed Solutions -> Optional Reference-Chain Follow-up Choices -> Next Deep-Dive Issue Categories (Not commands).

Core workflow

  1. Reuse current-session results and valid .rsdoctor-analysis-cache.json entries before doing new work.
  2. Locate rsdoctor-data.json fast: user-provided path, then dist/rsdoctor-data.json, output/rsdoctor-data.json, static/rsdoctor-data.json, .rsdoctor/rsdoctor-data.json, then one bounded rg --files search excluding node_modules and .git. Treat manifest.json only as an index.
  3. If data exists, skip all plugin version/config/build generation logic. Update cache when useful.
  4. If data is missing, stop analysis: do not run rsdoctor-agent analysis commands, do not run the Analysis Gate, and either ask for the data path or run the Generation Gate below only when setup/generation is required.
  5. After a real data file exists, run Analysis Gate at most once before the first rsdoctor-agent data-fetch command: verify global @rsdoctor/agent-cli with npm view @rsdoctor/agent-cli version and rsdoctor-agent --version; install latest only if missing/outdated, a version-related error occurs, or the user asks to refresh.
  6. Fetch only the Default Evidence Set first; run independent fetches in parallel when possible.
  7. Run the ROI Triage Gate below before selecting deep-dive commands or recommendations. Use it to rank issue categories by measured impact, then synthesize findings in the required response order.

Performance rules: parallelize independent checks, cache only derived facts (dataFile, dataFileMtime, pluginName, pluginVersion, dependency/config/plugin modification times), and invalidate cache when paths disappear, modification times change, the user asks to refresh, or cached values fail. Speculative plugin checks must not trigger generation; use them only after confirming the data file is missing.

ROI triage gate

Before recommending fixes, classify the current build into broad cost buckets and choose the highest-ROI lever from evidence, not intuition. This gate is generic for Rspack/Webpack projects; do not use framework-specific runtime layers unless the user's project exposes them in the data.

Cost bucketEvidence sourceFirst lever
Assets/mediaassetsTop, assets media, chunkGraph.assetsCompress, convert, deduplicate, subset, or lazy-load large assets
Large packages/modulespackagesTop, module/package size fieldsReplace heavy packages, use deep imports, split non-critical code, or review direct dependency choices
Duplicate/cross-chunk costE1001/E1002, packages duplicates, cross-chunk package summariesDeduplicate versions, tune package resolution, or adjust splitChunks/cache groups
Tree-shaking wasteretainedModulesTop, retained CJS/barrel/side-effects modulesFix CJS/barrel imports, sideEffects declarations, or package entrypoints
Build-time costbuildCost, loaders/plugins/directories cost dataOptimize loaders/plugins, cache, watcher, or source-map/dev settings; prioritize only when the user asks about build performance

Decision rules:

  • Report the measured breakdown first when it changes recommendation priority.
  • Start with the largest bucket that maps to a practical fix; a smaller issue should not outrank a larger one unless the larger one is expected or intentionally unavoidable.
  • Treat issuer/reference-chain tracing as second-pass work. Run it only when a high-impact candidate needs ownership evidence, or when the user asks "why" / "who imported this".
  • Do not present aggregate rule output as sufficient evidence for a fix that requires a concrete file, package, chunk, size, or dependency path.
  • If the largest bucket is structural or intentionally required, say that it is a wall and name the external change that would be needed instead of inventing low-impact source edits.

Generation gate

Identify pluginName (@rsdoctor/rspack-plugin or @rsdoctor/webpack-plugin) and determine pluginVersion from local files first: package.json, lockfile, then node_modules/<plugin>/package.json; use pnpm why / npm ls only as fallback.

Use this exact if/else decision tree; do not merge branches:

text
if pluginName is missing:
  install/register the matching Rsdoctor plugin, then configure output.mode='brief' and output.options.type=['json']; build with RSDOCTOR=true only
else if pluginVersion is unknown:
  resolve pluginVersion first; if still unknown, configure output.mode='brief' and output.options.type=['json']; build with RSDOCTOR=true only
else if pluginVersion >= 1.5.11:
  do not edit plugin config just for JSON; build with RSDOCTOR_OUTPUT=json and RSDOCTOR=true if needed
else: # pluginVersion < 1.5.11
  MUST configure output.mode='brief' and output.options.type=['json']; build with RSDOCTOR=true only

Preflight every build command: RSDOCTOR_OUTPUT=json is allowed only in the pluginVersion >= 1.5.11 branch. For missing, unknown, or < 1.5.11, it is forbidden. For < 1.5.11, generating rsdoctor-data.json requires the plugin config below:

ts
output: {
  mode: 'brief',
  options: {
    type: ['json'],
  },
}

Evidence and command bounds

Default Evidence Set:

Summary keyEvidence sourceBounds
buildCostbuild summaryfiltered fields only
assetsToptop assets by raw/gzip sizefixed Top-N
packagesToptop packages by gzip sizefixed Top-N; avoid full packages list pages
duplicatePackagesE1001 duplicate package summaryfirst-pass summary only
crossChunkPackagesE1002 cross-chunk duplication summaryfirst-pass summary only
retainedModulesToptree-shaking retained-modules --limit 10filtered fields only; no --compact

Scope rules:

  • Use rsdoctor-agent for bundle data access only after rsdoctor-data.json exists; prefer parallel independent fetches; bound output with --filter, pagination, and --limit.
  • Default analysis stays within the Default Evidence Set. For non-default analysis, choose minimal fields from references/rsdoctor-data-types.md and patterns from references/common-analysis-patterns.md.
  • Treat chain tracing, broad commands, optimization edits, splitChunks experiments, and build re-runs as opt-in follow-ups that require user confirmation.
  • For duplicate packages and tree-shaking issues, identify issues first; trace reference/import chains only after user confirmation.
  • Prefer tree-shaking retained-modules --emitted-only --category side-effects --limit 10 with narrow --filter for side-effects investigations.
  • For retained emitted modules, use tree-shaking retained-modules with --emitted-only, bounded --category, --sort gzipSize, --limit, and narrow --filter; do not pass --compact.
  • Use tree-shaking summary only as fallback for missing fields or aggregate context. Treat tree-shaking bailout-reasons as high-volume; run it only when explicitly requested and pass target --modules (max 100).
  • If any command exceeds 5k tokens, 500 KB raw output, or a few hundred transcript lines, stop broad fetching and switch to targeted compact queries.

Output and recovery

Output format:

  1. Issues found in the current build and recommended fixes:
    • Group each issue with its fix recommendation.
    • Include concrete evidence (size/time/count/path/rule code) and priority.
    • For duplicate packages and tree-shaking issues, include a short "continue tracing vs stop here" choice.
  2. Whether deeper analysis is still needed:
    • List remaining issue categories only, not commands.

For Top-N insights, prefer a table: Name | Volume/Time | Count | Recommendation.

Recovery rules:

  • rsdoctor-data.json missing: do not run rsdoctor-agent; ask for the data path or run Generation Gate, then use the matching install reference if setup is needed.
  • Command not found: run Analysis Gate, then retry with rsdoctor-agent.
  • query reports unknown tool: run list and use a catalog tool name, or switch to direct <group> <subcommand> mode.
  • JSON read error: verify file path, JSON validity, and permissions.
  • Run installs, builds, version checks, and rsdoctor-agent... commands only in the host's authorized command environment when it has the required project, dependency, and network access. If the available environment lacks that access, stop and ask the user instead of attempting to bypass the sandbox or permission boundary. Clearly identify the missing permission or access; if an Rsdoctor dependency must be installed, tell the user which dependency is required and provide the appropriate package-manager command for them to run.

References: commands/options references/command-map.md; install/config/data location references/install-rsdoctor.md, references/install-rsdoctor-rspack.md, references/install-rsdoctor-webpack.md, references/install-rsdoctor-common.md; raw data fields references/rsdoctor-data-types.md; common patterns references/common-analysis-patterns.md.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Rsdoctor Analysis AI skill do?

Use when analyzing Rspack/Webpack bundles from local `rsdoctor-data.json` and producing evidence-based optimization recommendations.

Why use Rsdoctor Analysis on TypingMind?

Because you install it once and use it with any model. Rsdoctor Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Rsdoctor Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rstackjs/agent-skills/tree/main/skills/rsdoctor-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Rsdoctor Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Rsdoctor Analysis?

As many as you like. As long as a model supports skills, you can use Rsdoctor Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Rsdoctor Analysis AI skill free?

Yes. It is published on GitHub by rstackjs under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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