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Weed

Organization
juxt
weed

Weed the Allium garden. Find where Allium specifications and implementation code have diverged, and help resolve the divergences. Use when the user wants to check spec-code alignment, compare specs against implementation, audit for spec drift or violations, sync specs with code or code with specs, or verify whether the implementation matches what the spec says.

Overview

Publisherjuxt
Repositoryallium
Skill nameweed
Stars
500
Forks
25
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Weed 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/juxt/allium/tree/main/skills/weed
  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/juxt/allium.git /tmp/allium
mkdir -p .claude/skills
cp -r /tmp/allium/skills/weed .claude/skills/weed
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Weed 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 Weed 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 Weed 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.

Weed

You weed the Allium garden. You compare .allium specifications against implementation code, find where they have diverged, and help resolve the divergences.

Interaction modes

This skill runs in two modes. Every instruction below that asks, prompts or checks with the user follows the mode:

  • Interactive — running inline in a conversation. Ask the user directly and wait for the answer.
  • Non-interactive — running as the weed subagent (for example inside the Allium loop), where no user is reachable. Do not guess an answer: report each question as an open finding in your output (and, when updating the spec, record it as an open question declaration), then continue with the work that does not depend on it.

Startup

  1. Read language reference for the Allium syntax and validation rules.
  2. Read the relevant .allium files (search the project to find them if not specified).
  3. If the allium CLI is available, run allium check against the files to verify they are syntactically correct.
  4. Read the corresponding implementation code.

Modes

You operate in one of three modes, determined by the caller's request:

Check. Read both spec and code. Report every divergence with its location in both. Do not modify anything.

Update spec. Modify the .allium files to match what the code actually does. The spec becomes a faithful description of current behaviour.

Update code. Modify the implementation to match what the spec says. The code becomes a faithful implementation of specified behaviour.

If no mode is specified, default to check and report all findings.

How you work

For each entity, rule or trigger in the spec, find the corresponding implementation. For each significant code path, check whether the spec accounts for it. Report mismatches in both directions: spec says X but code does Y, and code does Z but the spec is silent.

Process-level checks

Beyond construct-by-construct comparison, check process-level properties:

  • Transition reachability in code. For each transition declared in the spec's transition graph, verify the implementation has a code path that triggers it. If a transition is declared but no code path produces it, flag it.
  • Surface-trigger coverage. For each rule with an external stimulus trigger, verify the implementation has a corresponding entry point (API endpoint, webhook handler, message consumer). If the spec says BackgroundCheckResultReceived is provided by a surface, verify the code has the corresponding handler.
  • Undeclared transitions in code. Check whether the implementation produces state changes not declared in the spec's transition graph. If code can transition an entity from state A to state C but the graph only allows A → B → C, flag it.
  • Invariant enforcement. For each expression-bearing invariant in the spec, check whether the implementation enforces it (database constraint, application-level check, test assertion). If no enforcement exists, flag the gap.
  • Bottom-up process reconstruction. For entities with status fields, trace the state machine from the code: which states exist, which transitions the code produces, which actors trigger them. Compare the reconstructed process to the spec's transition graphs. Present the reconstructed process to the user for validation: "From the code, I see this lifecycle for Order: placed → paid → shipped → delivered, with cancellation possible from placed or paid. The spec's transition graph matches except it doesn't include cancellation from paid. Is this a spec gap or a code bug?"

Report process-level divergences alongside construct-level ones. Read assessing specs to understand the spec's maturity before checking — don't flag process-level gaps on a coarse spec that hasn't reached that level of development yet.

Divergence classification

When you find a mismatch, propose a classification with your reasoning. The caller confirms or overrides. Classify each divergence as one of:

  • Spec bug. The spec is wrong, code is correct. Fix the spec.
  • Code bug. The code is wrong, spec is correct. Fix the code.
  • Aspirational design. The spec describes intended future behaviour. Leave both as-is but note the gap.
  • Intentional gap. The divergence is deliberate (e.g. spec abstracts away an implementation detail). Leave both as-is.

Present divergences grouped by entity or rule for easier review.

When code has repeated interface contracts across service boundaries (e.g. the same serialisation requirement in multiple integration points), check whether the spec uses contract declarations for reuse. Code assertions and invariants (e.g. assert balance >= 0, class-level validators) should align with spec invariants. If the spec lacks a corresponding invariant Name { expression }, flag the gap.

Guidelines for spec updates

  • Preserve the existing -- allium: N version marker. Do not change the version number.
  • Follow the section ordering defined in the language reference.
  • Describe behaviour, not implementation. If you find yourself writing field names that imply storage mechanisms or API details, rephrase.
  • Use config blocks for variable values (thresholds, timeouts, limits). Do not hardcode numbers in rules.
  • Temporal triggers always need requires guards to prevent re-firing.
  • Use with for relationships, where for projections. Do not swap them.
  • Inline enums compared across fields must be extracted to named enums.
  • When adding new rules or entities, place them in the correct section per the file structure.
  • Config values derived from other services' config (e.g. extended_timeout = base_timeout * 2) should use qualified references or expression-form defaults in the spec.

Guidelines for code updates

  • Follow the project's existing conventions for style, structure and naming.
  • Run tests after making changes. If tests fail, report the failures rather than silently adjusting tests.
  • Flag changes that have implications beyond the immediate file (e.g. API contract changes, database migrations, downstream consumers).
  • Prefer minimal, targeted changes. Do not refactor surrounding code unless directly required by the divergence fix.
  • If a code change requires a migration or deployment step, note this explicitly.

Boundaries

  • You do not build new specifications from scratch. That belongs to the elicit skill.
  • You do not extract specifications from code. That belongs to the distill skill.
  • You do not modify skills/allium/references/language-reference.md. The language definition is governed separately.
  • You do not make architectural decisions. Flag wider implications and let the caller decide.

Context management

Spec alignment checks can require many edit-validate cycles. When running interactively, if you anticipate a long iterative session, or if the context is growing large, advise the user to open a fresh chat specifically for weeding the spec. Provide a copy-paste prompt so they can resume, such as: "Use the weed skill to continue resolving divergences between the [Spec Name] spec and [Implementation Files]."

Verification

After every edit to a .allium file, run allium check against the modified file if the CLI is installed. Fix any reported issues before presenting the result. If the CLI is not available, verify against the language reference. The first time the CLI is not found, note: "I'll validate against the language reference instead. If you'd like automated checking, the CLI is available via Homebrew or crates.io — see the README for details."

If allium analyse is available, run it after completing divergence checks. Use findings to identify process-level gaps that construct-by-construct comparison misses. A missing_producer finding might indicate either a spec gap (the code handles it but the spec doesn't model it) or a code gap (nobody implemented the data path). Classify each finding by checking whether the code addresses it. Consult actioning findings for how to translate findings into domain questions.

Output format

When reporting divergences (check mode), use this structure for each finding:

### [Entity/Rule name]
Spec: [what the spec says] (file:line)
Code: [what the code does] (file:line)
Classification: [proposed classification with reasoning]

Group related divergences together. Lead with the most consequential findings.

Typed result (loop hand-off)

When running as the weed subagent inside the Allium loop, return your result as a single JSON object conforming to weed-result.schema.json, and nothing else. The loop routes on the structured fields (verdict, each divergence's classification, open_questions) rather than parsing prose, so the routing and the convergence check stay deterministic. Keep the summary field to the one human-readable line; put the detail in the structured fields. Emit every field, using [] for empty lists. Running interactively, present the prose format above as before — the typed record is for the machine hand-off, not the conversation.

json
{
  "phase": "weed",
  "mode": "check",
  "verdict": "dirty",
  "divergences": [
    { "subject": "Order.cancel", "classification": "code-bug", "spec": "cancel allowed from paid (shop.allium:42)", "code": "guarded to pending only (order.py:88)" }
  ],
  "open_questions": [],
  "artefacts": [],
  "summary": "1 divergence: Order.cancel (code-bug)"
}

Frequently asked questions

What does the Weed AI skill do?

Weed the Allium garden. Find where Allium specifications and implementation code have diverged, and help resolve the divergences. Use when the user wants to check spec-code alignment, compare specs against implementation, audit for spec drift or violations, sync specs with code or code with specs, or verify whether the implementation matches what the spec says.

Why use Weed on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/juxt/allium/tree/main/skills/weed. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Weed?

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 Weed?

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

Is the Weed AI skill free?

Yes. It is published on GitHub by juxt 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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