Rw Check Org Details logo

Rw Check Org Details

Organization
runwayml
rw-check-org-details

Query the Runway API for organization details: rate limits, credit balance, usage tier, and daily generation counts

Overview

Publisherrunwayml
Repositoryskills
Skill namerw-check-org-details
Stars
68
Forks
17
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 runwayml on GitHub. Read the source before you install it.

Installation

Install the Rw Check Org Details 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/runwayml/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/rw-check-org-details .claude/skills/rw-check-org-details
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rw Check Org Details 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 Rw Check Org Details 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 Rw Check Org Details 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.

Check Organization Details

Prerequisite: RUNWAYML_API_SECRET must be available to the server-side process. Never print or paste its value.

Query the Runway API to retrieve the user's organization details — credit balance, usage tier, rate limits, current daily generation counts, and historical credit usage.

Step 1: Verify API Key Is Available

Before making any requests, confirm the API key is accessible:

  1. Check whether the project loads RUNWAYML_API_SECRET from its server-side environment.
  2. Or check the current shell without printing the value: test -n "$RUNWAYML_API_SECRET".

If the key is not found, ask the user to create one in Runway Developer Portal settings and add it to the server-side environment, then stop.

Step 2: Query Organization Info

Call GET /v1/organization to retrieve the org's tier, credit balance, and current usage.

Node.js

javascript
import RunwayML from '@runwayml/sdk';

const client = new RunwayML();
const details = await client.organization.retrieve();
console.log(JSON.stringify(details, null, 2));

Python

python
from runwayml import RunwayML

client = RunwayML()
details = client.organization.retrieve()
print(details)

cURL / fetch (no SDK)

bash
curl -s https://api.dev.runwayml.com/v1/organization \
  -H "Authorization: Bearer $RUNWAYML_API_SECRET" \
  -H "X-Runway-Version: 2024-11-06" | python3 -m json.tool

Response Shape

json
{
  "tier": {
    "maxMonthlyCreditSpend": 10000,
    "models": {
      "gen4.5": {
        "maxConcurrentGenerations": 2,
        "maxDailyGenerations": 200
      }
    }
  },
  "creditBalance": 5000,
  "usage": {
    "models": {
      "gen4.5": {
        "dailyGenerations": 12
      }
    }
  }
}

Step 3: Present the Results

Format the output as a clear summary for the user:

## Organization Overview

**Credit Balance:** X credits ($X.XX at $0.01/credit)
**Monthly Spend Cap:** X credits

### Rate Limits (by model)

| Model | Concurrency | Daily Limit | Used Today | Remaining |
|-------|-------------|-------------|------------|-----------|
| gen4.5 | 2 | 200 | 12 | 188 |
| veo3.1 | 2 | 100 | 5 | 95 |
| ... | ... | ... | ... | ... |

Key things to highlight:

  • Credit balance — convert to dollar value (credits × $0.01)
  • Per-model daily limits — show how many generations remain today (rolling 24-hour window)
  • Concurrency — how many tasks can run simultaneously per model
  • Monthly cap — the max credit spend per month for their tier

Step 4 (Optional): Query Credit Usage History

If the user wants to see historical usage, call POST /v1/organization/usage.

Node.js

javascript
const usage = await client.organization.retrieveUsage({
  startDate: '2026-02-15',   // ISO-8601, up to 90 days back
  beforeDate: '2026-03-17'   // exclusive end date
});
console.log(JSON.stringify(usage, null, 2));

Python

python
usage = client.organization.retrieve_usage(
    start_date="2026-02-15",
    before_date="2026-03-17"
)
print(usage)

cURL / fetch (no SDK)

bash
curl -s -X POST https://api.dev.runwayml.com/v1/organization/usage \
  -H "Authorization: Bearer $RUNWAYML_API_SECRET" \
  -H "X-Runway-Version: 2024-11-06" \
  -H "Content-Type: application/json" \
  -d '{"startDate": "2026-02-15", "beforeDate": "2026-03-17"}' \
  | python3 -m json.tool

Response Shape

json
{
  "results": [
    {
      "date": "2026-03-16",
      "usedCredits": [
        { "model": "gen4.5", "amount": 120 },
        { "model": "veo3.1", "amount": 400 }
      ]
    }
  ],
  "models": ["gen4.5", "veo3.1"]
}

Present this as a usage breakdown:

### Credit Usage (Feb 15 – Mar 17)

| Date | Model | Credits Used |
|------|-------|-------------|
| 2026-03-16 | gen4.5 | 120 |
| 2026-03-16 | veo3.1 | 400 |
| ... | ... | ... |

**Total:** X credits

Tier Reference

If the user asks about upgrading, share the tier breakdown:

TierConcurrencyDaily GensMonthly CapUnlock Requirement
1 (default)1–250–200$100
23500–1,000$5001 day + $50 spent
351,000–2,000$2,0007 days + $100 spent
4105,000–10,000$20,00014 days + $1,000 spent
52025,000–30,000$100,0007 days + $5,000 spent

Tiers upgrade automatically once the spend and time requirements are met.

Troubleshooting

IssueCauseFix
401 UnauthorizedInvalid or missing API keyReplace RUNWAYML_API_SECRET with a valid Developer Portal key
creditBalance is 0No credits purchasedPurchase at https://dev.runwayml.com/ → Billing (min $10)
Daily limit reachedRolling 24-hour quota exhaustedWait for the window to reset, or upgrade tier
All models show 0 daily limitTier 1 restrictionsCheck that credits have been purchased

Frequently asked questions

What does the Rw Check Org Details AI skill do?

Query the Runway API for organization details: rate limits, credit balance, usage tier, and daily generation counts

Why use Rw Check Org Details on TypingMind?

Because you install it once and use it with any model. Rw Check Org Details 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 Rw Check Org Details in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/runwayml/skills/tree/main/skills/rw-check-org-details. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Rw Check Org Details?

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 Rw Check Org Details?

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

Is the Rw Check Org Details AI skill free?

Yes. It is published on GitHub by runwayml 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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