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Swamp

Organization
swamp-club
swamp

Swamp CLI — create and run models, build and validate workflows, query and manage data, store and retrieve vault secrets, develop and publish extensions, initialize repos, run reports, file issues, and troubleshoot errors. Triggers on swamp commands (swamp model, swamp workflow, swamp vault, swamp data), extension development, repo setup, or diagnostic questions. Do NOT use for getting started / onboarding (use swamp-getting-started), pull requests, git operations, worktree management, cron/agent scheduling, or general coding tasks unrelated to swamp.

Overview

Publisherswamp-club
Repositoryswamp
Skill nameswamp
Stars
647
Forks
46
Bundled files
103
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.

  • 103 bundled files

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

  • Open source

    Published by swamp-club on GitHub. Read the source before you install it.

Installation

Install the Swamp 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/swamp-club/swamp/tree/main/.claude/skills/swamp
  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/swamp-club/swamp.git /tmp/swamp
mkdir -p .claude/skills
cp -r /tmp/swamp/.claude/skills/swamp .claude/skills/swamp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Swamp 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 Swamp 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 Swamp 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.

Swamp

Core Concepts

  • Models — typed resource definitions exposing methods (create, stop, destroy, sync).
  • Data — versioned state snapshots produced by method runs; referenced via CEL expressions.
  • Workflows — declarative DAGs chaining model methods with guard expressions for idempotent execution and assert steps for validation.
  • Vaults — secret storage referenced by models at runtime.
  • Extensions — TypeScript packages adding model types, vault backends, datastores, reports, and webhook handlers.
  • Grants — authorization rules for swamp serve access control.
  • Serve — exposes the repo over the network with TLS and authentication.

Routing Table

Route to the right guide based on what the user needs.

User intentGuide
Models — create, run, edit, delete, search typesreferences/model/guide.md
Workflows — create, run, validate, DAG, historyreferences/workflow/guide.md
Data — list, query, versions, GC, deletereferences/data/guide.md
Vaults — create, store/read secrets, expressionsreferences/vault/guide.md
Reports — run, configure, view, filterreferences/report/guide.md
Repository — init, upgrade, datastores, sourcesreferences/repo/guide.md
Extensions — create models/vaults/datastores/reports/webhooksreferences/extension/guide.md
Publishing — push extensions to registry, deprecatereferences/extension-publish/guide.md
Issues — file bugs, features, security reportsreferences/issue/guide.md
Run tracking — active runs, stale detection, diagnosticsreferences/model/guide.md
Architecture — which primitive to use, design trade-offsreferences/architecture/guide.md
Sharing — promote solo repo to team, datastore + vault setupreferences/share/guide.md
Serve — auth, grants, access control, tokens, OAuthreferences/serve/guide.md
Troubleshooting — errors, health checks, diagnosticsreferences/troubleshooting/guide.md

Common Commands

bash
# Models
swamp model create <type> <name>               # create a model definition
swamp model @<type> method run <method> <name> # run a method on a model
swamp model get <name> --json                  # inspect current model state
swamp model type search [query]                # find available model types
swamp model list                               # list all models in the repo

# Data
swamp data list <name>                         # list data versions for a model
swamp data query '<CEL predicate>'             # query data with CEL expressions
# read one item's latest content (`swamp data get` is deprecated)
swamp data query 'model("<model>") && name == "<data>"' --select content

# Workflows
swamp workflow create <name>                   # create a new workflow
swamp workflow validate <name>                 # validate DAG before running
swamp workflow run <name>                      # execute a workflow
swamp workflow run <name> --input key=value    # execute with inputs
swamp workflow resume <name>                   # resume a suspended workflow
swamp workflow resume <name> --run <id>        # retry a failed run's failed steps
swamp workflow resume <name> --from <step>     # re-enter failed run at step
swamp workflow history search --json           # search run history

# Run tracking
swamp run history                              # recent runs (last 24h)
swamp run history --active                     # what's running right now?
swamp run doctor                               # diagnose stale/orphaned runs
swamp run doctor --fix                         # auto-reap stale runs

# Vaults, Reports, Extensions
swamp vault create <type> <name>               # create a vault for secrets
swamp report run <name>                        # run a report
swamp extension init <name>                    # scaffold a new extension

Workflow

  1. Route — match intent to a guide in the routing table.
  2. Load — read the guide; load its reference.md if needed, deeper references/ only when the guide says to.
  3. Validate — before running any workflow: swamp workflow validate <name>. Before destructive methods (delete, stop, destroy): swamp model get <name> --json to confirm the target exists and is in the expected state. Proceed only when validation passes.
  4. Verify args — run swamp help <subcommand> (e.g. swamp help model method run) to confirm exact flags and arguments before executing. The output is structured JSON — the canonical CLI schema.
  5. Execute — run the command.
  6. On failure — load references/troubleshooting/guide.md and diagnose before retrying or changing definitions.

Rules

  1. Use the routing table, not memory. Don't answer from cached knowledge about swamp commands — always load the current guide.
  2. Consult the architecture guide for design decisions. Before choosing between primitives (model vs. workflow vs. extension vs. report) or explaining a design trade-off, load references/architecture/guide.md and cite the design doc you relied on.
  3. Use swamp commands, don't go around them. Query data with swamp data query, not by grepping .swamp/ files. Interact with resources through model methods, not raw CLI tools when a model type already wraps the API — check with swamp model type search. Composing with --json output (e.g. piping through jq) is fine — the anti-pattern is bypassing swamp entirely.
  4. Verify flags with swamp help before executing. swamp help <command> outputs the full CLI schema as structured JSON — every subcommand, argument, and option. Always run it to confirm exact flags before executing a swamp command (workflow step 4). Do not guess flags from memory.

Bundled files

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

and 43 more files.

Frequently asked questions

What does the Swamp AI skill do?

Swamp CLI — create and run models, build and validate workflows, query and manage data, store and retrieve vault secrets, develop and publish extensions, initialize repos, run reports, file issues, and troubleshoot errors. Triggers on swamp commands (swamp model, swamp workflow, swamp vault, swamp data), extension development, repo setup, or diagnostic questions. Do NOT use for getting started / onboarding (use swamp-getting-started), pull requests, git operations, worktree management, cron/agent scheduling, or general coding tasks unrelated to swamp.

Why use Swamp on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/swamp-club/swamp/tree/main/.claude/skills/swamp. 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 Swamp?

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 Swamp?

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

Is the Swamp AI skill free?

It is published on GitHub by swamp-club. Check the repository for licensing terms. 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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