Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/celigo/ai/getting-startednpx skills add celigo/ai --skill getting-startedgit clone --depth 1 https://github.com/celigo/aiWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00050 | $0.02918 |
| Opus 5 | $0.00025 | $0.01459 |
| Sonnet 5 | $0.00010 | $0.00584 |
| Haiku 4.5 | $0.00005 | $0.00292 |
Grade A, and why
getting-started scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Getting Started with Celigo Integrations
Core Concepts
Celigo integrations move data between external systems through a small set of resource types:
- Connection -- credentials and configuration that authenticate to an external system (Salesforce, NetSuite, HTTP API, database, FTP, etc.)
- Export -- data source step that fetches records from a connected system (or receives them via webhook)
- Import -- data destination step that writes records to a connected system
- Flow -- pipeline that connects exports to imports, with optional branching, transformation, and scripting
- Integration -- named container that groups related flows, connections, and resources
- Script -- JavaScript hook that runs at specific points in the data pipeline (preSavePage, preMap, postMap, postSubmit, postResponseMap)
- API -- custom HTTP endpoint that exposes integration logic for synchronous external consumption
- Tool -- reusable building block (input schema -> routers with lookups/imports -> output contract) callable from flows, APIs, AI agents, MCP servers, and other tools
- AI agent -- LLM-powered pipeline step (stored as an import) that classifies, extracts, summarizes, or generates data mid-pipeline
- Guardrail -- safety/compliance check (PII, moderation, AI evaluation) that flags records; the parent pipeline decides what happens to flagged records
- Lookup cache -- account-level key-value store for fast in-memory reference lookups (cross-reference IDs, large translation tables, dedup markers)
How each surface is invoked
The three pipeline-carrying resources differ mainly in what starts them:
| Resource | Started by | Schedule/listeners | Runtime controls |
|---|---|---|---|
| Flow | Itself -- cron schedule, listener/webhook, or another flow chaining into it | Yes | Yes (proceedOnFailure, skipRetries, chaining, ...) |
| API | An external HTTP caller; the request IS the source record | No | No -- errors land on the fail response; retries are the caller's concern |
| Tool | A consumer -- flow step, AI agent, API, MCP server, or another tool | No | No -- the consumer decides error behavior |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 166 lines · 50 tokens per session scan A 66447a061203
getting-started is a skill published in the GitHub repository celigo/ai (3 stars, last pushed 4d ago), licensed MIT. It adds 50 tokens to every session and 2,918 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
prepare-mcp-integration
Prepare MCP/vendor integrations through cited research, a content PR, exact-revision human approval, and one governed Paperclip connector PR per approved connection. Use for new integration research and delivery; not for ad hoc connector coding that bypasses the playbooks.
webhook-subscriptions
Webhook subscriptions: event-driven agent runs.
agent-builder
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creating-skills
Use when creating new agent skills or improving existing ones - ensures skills are discoverable, scannable, and effective through proper structure, discovery optimization, and real examples.
running-and-testing-locally
Use when running the Nango application locally for development and browser testing - covers Docker services, dev commands, service URLs, and troubleshooting startup issues.
creating-integration-docs
Use when adding or editing Nango integration documentation - creates and maintains integration pages, setup guides, connect guides, navigation, and provider metadata following established patterns.