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Ade

OrganizationPopular
landing-ai
ade

Parse documents and extract schema-shaped data with the ADE (Agentic Document Extraction) v2 APIs through the ade CLI. A local job-item store makes every run idempotent, resumable, and citable — repeat runs are free, interrupted runs resume, and every answer can cite element ids with visual evidence.

Overview

Publisherlanding-ai
Repositoryade-cli
Skill nameade
Stars
2.4K
Forks
255
Bundled files
96
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.

  • 96 bundled files

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

  • Open source

    Published by landing-ai on GitHub. Read the source before you install it.

Installation

Install the Ade 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/landing-ai/ade-cli.git \
  .claude/skills/ade
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ade 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 Ade 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 Ade 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.

ade — agent skill

ade drives the ADE (Agentic Document Extraction) v2 document APIs (parse, extract) and persists everything it produces in a local store under ~/.ade. You are the judgment; the CLI is deterministic plumbing: it parses, persists, searches, renders, and totals — it never ranks or selects for you.

First move

ade help --json

One call returns the whole shipped surface: every command and flag, each verb's --json result shape, the exit states, and the store layout. Prefer it over N --help round trips. ade help workflow (also output, credentials, errors) is the conceptual page behind it. Check credentials with ade auth status --json; if logged out, set ADE_API_KEY, or pipe a key in (echo $KEY | ade auth login) — no terminal required either way. An API key acts in the organization it was created in; browser (OAuth) sessions carry their own selection — status reports it, ade auth org list --json shows the memberships, and ade auth org switch <org> --env <env> changes it. If a run must bill to a specific organization, confirm the selection before parsing: re-running the same invocation after a switch joins the recorded run instead of re-billing under the new organization.

Conventions

  • Always pass --json. Every command emits one stable JSON object/array on stdout — errors and pending payloads included. Human text is for terminals, not for parsing. The whole result is on stdout: the extraction, the matches, the paths. Reading the store is a convenience, never a requirement — and ade help --json publishes each verb's shape, so you never have to discover it by running.
  • --id-only when you only need the id. parse, extract, and find take it: the id(s), one per line, nothing else — JOB=$(ade parse -d report.pdf --id-only). Errors go to stderr, so a captured id is never a sentence.
  • Job item ids are the currency. Every parse or extract run is a job item: one folder under ~/.ade/jobs/<job-item-id>/, keyed by the invocation (verb × environment × source × content × params; URL sources are keyed by the URL alone — no content component, so remote drift does not re-key). Commands accept an unambiguous id prefix. Changing the file, its path, or any param mints a sibling item — nothing is silently replaced.
  • Commands are guarantees. parse means "ensure this exact run exists", not "fire a request". Re-running an already-done invocation is free — served from disk with an already parsed notice and "cached": true. Only --force re-bills.

The loop

  1. Parse (free if already done):

    ade parse -d report.pdf --json

    The payload carries job_item_id — hold onto it. Everything else keys off it. The markdown and the elements projection stay on disk unless you ask for them: --include markdown, --include elements.

  2. Discover element ids (local, instant, no API call):

    ade find JOB_ITEM_ID "total revenue" --json
    ade find JOB_ITEM_ID --type table_cell --page 3 --json

    (--job JOB_ITEM_ID is the equivalent flag spelling — repeat it to search several items in one call.)

    Matches are citation records: {job_item_id, element_id, type, page, box, text}. Filters compose (AND); results come in document order, never ranked — selection is your job.

  3. Extract structured data against a JSON Schema:

    ade extract JOB_ITEM_ID --schema schema.json --json

    The result is its own job item. The payload's extraction key is the schema-shaped result itself — read it there, not off disk. Per-field evidence (evidence, persisted as evidence.json) joins extraction spans to element ids, pages, and boxes — non-empty fields the model synthesised rather than quoted are flagged ungroundable; empty-valued fields (blank cells, absent optionals — nothing to ground) are labeled empty. Neither is ever silently dropped.

  4. Cite and show. ade view JOB_ITEM_ID --json builds a self-contained HTML viewer; deep links are the citation contract:

    ade view JOB_ITEM_ID --element-id ELEMENT_ID --json   # emits view.html#element=ELEMENT_ID
    ade crop JOB_ITEM_ID --element-id ELEMENT_ID --json   # PNG of that element's region
    ade crop JOB_ITEM_ID --type figure --json             # every figure, one command

    End answers with one deep link per job item, citing element ids. Use crop when you need to look at evidence mid-reasoning — it takes find's own filters (--type, --page, --all), so a selection crops in one call and returns crops[]; never loop find into crop yourself.

Reuse posture — parse bills once

Every parse the CLI ever runs is a reusable job item. Given a document path, extract -d FILE --schema … reuses the latest completed parse of that path+content (logged in the summary; no parse billed). If none exists, it runs a standalone parse first — a normal, top-level parse item, exactly as if you had run parse -d — then the extraction referencing it: two billable runs, both itemised. Repeat extract -d runs of the same file then reuse that parse, so it bills exactly once.

Prefer the explicit two-step (parse -d, then extract JOB_ITEM_ID) when you want the same parse to feed several schemas — the id makes the reuse visible.

Pending and resume

Wait expiry is a normal outcome, not an error. If the poll budget (--wait, default 600s) runs out, the command exits with code 3 and a {"status": "pending", "run_id": …, "job_item_id": …} payload while the run continues server-side (submitted work always completes and bills — there is no cancel). The recovery gesture is always the same command, re-run. A re-run joins the recorded run; it never resubmits and never re-bills. Interrupts (Ctrl-C) stop the waiting, not the work — same gesture. --wait 0 submits and returns immediately.

Submit-and-return, then collect later — the pending payload carries the id too, so this works in both steps:

JOB=$(ade parse -d report.pdf --wait 0 --id-only)   # exit 3, run continuing
ade parse -d report.pdf --json                       # re-run: resumes, never re-bills

Exit codes

CodeStateMeaning
0okSuccess — the payload is on stdout.
1failedThe run failed or the target cannot serve the request.
2usageThe invocation itself was wrong; nothing was submitted.
3pendingWait budget expired; the run continues server-side. Re-run the same command to resume.
4rate_limitedSubmit was rate-limited and the wait budget ran out before a run existed; nothing billed. Re-run to retry.

Reading the store directly

Optional, never required: every result is already on stdout. Artifacts are plain files — history list --json gives every item's records, and the summaries print each item's store path.

  • parse.json — raw ParseResponse (ground truth, verbatim)
  • parse.md — the parse markdown extraction spans index
  • elements.json — flat element records with inline grounding; boxes are normalized {xmin, ymin, xmax, ymax} fractions of page size in [0, 1]
  • extract.json — raw extraction result with per-field spans
  • evidence.json — the field→box join (element ids, pages, boxes)

One vocabulary note when reading these files: on-disk records (meta.json, job.json, parse/ref.json) spell the server-side run id as job_id — the wire contract's name for the same value --json payloads report as run_id. Neither is ever the job item id.

Prefer find over loading elements.json into context: it returns joined records, not lines. history clear JOB_ITEM_ID deletes an item; clearing a parse item cascades to the extractions referencing it.

Sharp edges

  • --options is a verbatim ParseOptions pass-through — the server rejects unknown keys with a 422, so consult ade help parse for the accepted keys rather than guessing.
  • Parse variants coexist. The same document parsed with different params is a sibling item, not a replacement. An extraction goes stale only when its exact parse item is re-run in place with --force.
  • Moving or editing a file changes identity — the next parse of it is a new job item that bills. The old item and its evidence stay intact and true of the run they came from.
  • --markdown extractions have no page evidence (there is no parse to join against) — evidence degrades to spans-only, and view renders the markdown pane alone.
  • URL parses have no local bytes, so page imagery renders from an attached copy: parse --document-url … --keep-copy fetches it at parse time (reliable — pre-signed URLs expire); otherwise the first view/crop downloads it automatically (--no-download skips; the payload records downloaded, and on a failed fetch view degrades to an empty preview with download_error while crop errors). Markdown, elements, and extractions work either way.

Bundled files

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

and 36 more files.

Frequently asked questions

What does the Ade AI skill do?

Parse documents and extract schema-shaped data with the ADE (Agentic Document Extraction) v2 APIs through the ade CLI. A local job-item store makes every run idempotent, resumable, and citable — repeat runs are free, interrupted runs resume, and every answer can cite element ids with visual evidence.

Why use Ade on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/landing-ai/ade-cli/tree/main. 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 Ade?

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 Ade?

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

Is the Ade AI skill free?

Yes. It is published on GitHub by landing-ai 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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