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Update Deps

CommunityPopular
backnotprop
update-deps

Audit and update npm/Bun dependencies with supply chain integrity checks — verifies maintainers, publish age, tarball diffs, and provenance before bumping. Defers risky packages to ~/.supply-chain/notes/.

Overview

Publisherbacknotprop
Repositoryplannotator
Skill nameupdate-deps
Stars
9.3K
Forks
701
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Update Deps 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.
    https://github.com/backnotprop/plannotator/tree/main/.agents/skills/update-deps
  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/backnotprop/plannotator.git /tmp/plannotator
mkdir -p .claude/skills
cp -r /tmp/plannotator/.agents/skills/update-deps .claude/skills/update-deps
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Update Deps 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 Update Deps 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 Update Deps 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.

Update Dependencies

Audit outdated packages, verify supply chain integrity, bump what's safe, defer what needs review, and log everything.

This process has three phases: discovery, integrity audit, and execution. The integrity audit is the most important — every package gets checked before it touches the lockfile.

Phase 1: Discovery

Run bun outdated to get the full list of packages with available updates.

bash
bun outdated

Parse the output into a structured list. For each package, note:

  • Package name
  • Current version
  • Available update version
  • Whether it's age-gated (indicated by * in the output — the footnote "The * indicates that version isn't true latest due to minimum release age" confirms this)
  • Whether it's a runtime dep, dev dep, or peer dep

Also check whether any packages are blocked entirely by the age gate (like @pierre/diffs was when its minimum semver range couldn't resolve). Flag these separately — they may need minimumReleaseAgeExcludes in bunfig.toml.

Phase 2: Integrity Audit

This is the core of the process. Spawn one Sonnet sub-agent per package to run the integrity check in parallel. Sonnet is used here because these are independent, structured verification tasks that don't need heavier reasoning.

Each sub-agent receives the package name, current version, and target version, and performs the following checks:

Sub-agent prompt template

For each outdated package, spawn a Sonnet agent with this task:

You are auditing the npm package "{package}" for a version bump from {current} to {target}.

Run these checks and report back with a JSON object:

1. **Maintainer verification**: Check if maintainers changed between versions.
   npm view {package}@{current} maintainers --json
   npm view {package}@{target} maintainers --json
   Compare the two lists. Flag any additions or removals.

2. **Publish date and age**: Get the publish timestamp.
   npm view {package} time --json
   Extract the date for version {target}. Calculate days since publication.
   Flag if younger than 7 days.

3. **Provenance**: Check if the package has registry signatures or attestations.
   npm audit signatures (if not already run this session)
   Note whether the package has provenance attestations.

4. **Tarball diff**: Show what actually changed in the published package.
   npm diff --diff={package}@{current} --diff={package}@{target}
   Summarize the changes:
   - How many files changed
   - Are changes limited to version bumps, deps, and rebuilt dist? Or are there meaningful source changes?
   - Any new runtime dependencies added?
   - Any suspicious patterns (obfuscated code, eval(), network calls in unexpected places)

5. **Release notes**: Try to find what changed.
   Check the package's repository for release notes or changelog:
   npm view {package}@{target} repository.url
   gh release view v{target} --repo <owner/repo> (try with and without v prefix)
   Summarize the changelog if found.

Report your findings as JSON:

{{
  "package": "{package}",
  "from": "{current}",
  "to": "{target}",
  "age_days": <number>,
  "maintainers_changed": <boolean>,
  "maintainers_added": [],
  "maintainers_removed": [],
  "provenance": <boolean>,
  "new_runtime_deps": [],
  "files_changed": <number>,
  "has_source_changes": <boolean>,
  "changelog_summary": "<brief summary>",
  "suspicious_patterns": [],
  "verdict": "safe" | "review" | "defer",
  "verdict_reason": "<one sentence explanation>"
}}

Verdict guidelines:
- "safe": Same maintainers, no new runtime deps, changes match what changelog describes, no suspicious patterns
- "review": Minor concerns (e.g., new maintainer who is clearly from the same org, small new dep from known publisher)
- "defer": Maintainer changes from unknown accounts, new runtime deps with unclear purpose, suspicious code patterns, substantive API changes that need integration testing

Collecting results

As sub-agents complete, collect their JSON reports. If a sub-agent fails or times out, mark that package as "defer" with reason "audit failed".

Tier classification

After collecting all results, classify packages into tiers. This helps the user understand the risk profile at a glance:

TierDescriptionTypical action
Runtime, high surfaceLibraries your code calls directly (parsers, diff engines, UI libs)Check provenance, review diff, run tests
SDK/API depsThird-party service SDKs (agent SDKs, platform integrations)Read changelog for API changes, test integration
Dev-onlyType definitions, build tools, test frameworksUpdate freely — these don't ship
Build toolchainBun itself, compilers, bundlersMost caution — breakage affects all outputs

Phase 3: Execution

Bump safe packages

For all packages with verdict "safe":

bash
bun update pkg1@version1 pkg2@version2 ...

If the update fails due to age gate conflicts (a package's minimum semver can't resolve), add it to minimumReleaseAgeExcludes in bunfig.toml and document why.

Log to supply chain notes

Write the full audit results to ~/.supply-chain/notes/<YYYY-MM-DD>.json:

json
{
  "date": "YYYY-MM-DD",
  "project": "plannotator",
  "bumped": [
    {
      "package": "...",
      "from": "...",
      "to": "...",
      "age_days": 0,
      "maintainers_changed": false,
      "provenance": false,
      "notes": "..."
    }
  ],
  "deferred": [
    {
      "package": "...",
      "current": "...",
      "available": "...",
      "age_days": 0,
      "maintainers_changed": false,
      "reason": "...",
      "review_by": "YYYY-MM-DD"
    }
  ],
  "excluded_from_age_gate": [
    {
      "package": "...",
      "reason": "..."
    }
  ]
}

Set review_by to 7 days from today for deferred packages.

Check previously deferred packages

Read all files in ~/.supply-chain/notes/ and collect any deferred packages from previous audits that are still at the same version in the current lockfile. These are packages that were deferred before and still haven't been updated.

To check: for each previously deferred entry, see if the current installed version matches the current field from the deferral note. If it does, the package is still deferred.

Phase 4: Recap

Present a clear summary to the user:

Updated

List each bumped package with version change and one-line reason it was safe.

Deferred

List each deferred package with version change and reason for deferral.

Still Deferred (from previous audits)

List any packages that were deferred in previous audit sessions and still haven't been bumped. Include the original deferral date and reason. This is the "you've been putting this off" section — it keeps deferred packages from being forgotten.

If the still-deferred list is empty, say so — that's a good sign.

Age Gate Exclusions

If any packages were added to minimumReleaseAgeExcludes, note them and why.

Frequently asked questions

What does the Update Deps AI skill do?

Audit and update npm/Bun dependencies with supply chain integrity checks — verifies maintainers, publish age, tarball diffs, and provenance before bumping. Defers risky packages to ~/.supply-chain/notes/.

Why use Update Deps on TypingMind?

Because you install it once and use it with any model. Update Deps 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 Update Deps in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/backnotprop/plannotator/tree/main/.agents/skills/update-deps. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Update Deps?

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 Update Deps?

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

Is the Update Deps AI skill free?

Yes. It is published on GitHub by backnotprop under the Apache-2.0 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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