Pipefy Observability

Organization

pipefy

pipefy-observability

Use this skill when the user wants to check AI agent logs, automation execution logs, org-level usage stats, AI credit consumption, or export automation job history.

Overview

Publisher	pipefy
Repository	`ai-toolkit`
Skill name	`pipefy-observability`
Stars	52
Forks	24
Bundled files	2
License	Apache-2.0
Links	Skill source Repository

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.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by pipefy on GitHub. Read the source before you install it.

Installation

Install the Pipefy Observability AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/pipefy/ai-toolkit/tree/main/plugins/pipefy/skills/pipefy-observability
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/pipefy/ai-toolkit/tree/main/plugins/pipefy/skills/pipefy-observability

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/pipefy/ai-toolkit.git /tmp/ai-toolkit
mkdir -p .claude/skills
cp -r /tmp/ai-toolkit/plugins/pipefy/skills/pipefy-observability .claude/skills/pipefy-observability

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pipefy Observability 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 Pipefy Observability 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 Pipefy Observability 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.

Observability

Read the MCP reference or CLI reference for the surface you are using. Load only the relevant reference.

Monitor AI agent and automation execution, usage stats, credit consumption, and export job history.

Identifiers reference

Full identifier map: observability identifiers.

Concept	What tools expect	How to obtain
Pipe for AI agent logs	`repo_uuid` — the pipe UUID	`get_pipe` with numeric `pipe_id`; use `pipe.uuid`.
Automation for logs	`automation_id` — numeric	`get_automations pipe_id=...`
Org for usage stats	`organization_uuid` — UUID or numeric org id	`get_organization` returns the `uuid`; a numeric id also works (resolved server-side). Execution-metrics / export take numeric `organization_id`.

Tools

Operation	Read-only	Purpose
`get_ai_agent_logs`	Yes	Execution history for a specific AI agent.
`get_ai_agent_log_details`	Yes	Single execution detail for an AI agent log entry.
`get_automation_logs`	Yes	Execution history for an automation (by automation ID).
`get_automation_logs_by_repo`	Yes	Automation logs filtered by pipe.
`get_agents_usage`	Yes	Org-level AI agent execution count and trends.
`get_automations_usage`	Yes	Org-level automation execution stats.
`get_automation_execution_metrics`	Yes	Per-automation execution metrics over `FIFTEEN_MINUTES`, `SIXTY_MINUTES`, `TWELVE_HOURS`, or `TWENTY_FOUR_HOURS`; partial success returns `partial_errors` for denied ids.
`get_ai_credit_usage`	Yes	AI credit consumption and remaining balance.
`export_automation_jobs`	No	Trigger async export of automation job history.
`get_automation_jobs_export`	Yes	Poll export job status (after `export_automation_jobs`).
`get_automation_jobs_export_csv`	Yes	Download finished automation-jobs export as CSV text.

Steps — diagnose a failing AI agent

Get the pipe UUID (not the numeric pipe ID):

Operation: get_pipe pipe_id=67890

Capture pipe.uuid from the response.
Fetch recent agent logs:

Operation: get_ai_agent_logs repo_uuid=<UUID> first=30
Identify the failed execution — look for status: failed entries.
Check credit usage if the agent stopped unexpectedly:

Operation: get_ai_credit_usage organization_uuid=123 period="current_month"
Fix and re-enable — update the agent config (see pipefy-ai-agents) and toggle status:

Operation: toggle_ai_agent_status with the agent UUID and active=true (SDK: agent_uuid; MCP: uuid).

Steps — export automation history as CSV

Trigger the export:

Operation: export_automation_jobs organization_id=123 period="current_month"
Poll for completion:

Operation: get_automation_jobs_export export_id=<EXPORT_ID>

Repeat until status is finished or failed.

A finished export also carries fileUrl, a signed link to the xlsx. It expires 1 hour after the read that returned it, and anyone holding it can download the file until then. When you hand it to the user, say that it expires in 1 hour; for a fresh link, poll again.
Fetch CSV text (when finished):

Operation: get_automation_jobs_export_csv export_id=<EXPORT_ID>

Success criteria

Agent logs show execution timestamps and statuses.
Credit usage shows remaining balance; no unexpected drops.
CSV export downloads successfully and contains expected automation history.

Failure modes

get_ai_agent_logs returns empty: use the pipe UUID (e.g., abc123-...), not the numeric pipe ID. Get UUID from get_pipe.
get_automation_jobs_export stays in processing: large exports take time. Wait at least 60 seconds between polls. If still processing after several minutes, retry the export trigger.
Credit usage shows 0 remaining: alert the user — AI features will stop working until credits are replenished. Escalate to the Pipefy admin.

Bundled files

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

references/cli.md1 KB
references/mcp.md1 KB

Frequently asked questions

What does the Pipefy Observability AI skill do?

Use this skill when the user wants to check AI agent logs, automation execution logs, org-level usage stats, AI credit consumption, or export automation job history.

Why use Pipefy Observability on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pipefy/ai-toolkit/tree/main/plugins/pipefy/skills/pipefy-observability. 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 Pipefy Observability?

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 Pipefy Observability?

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

Is the Pipefy Observability AI skill free?

Yes. It is published on GitHub by pipefy 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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Pipefy Observability

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Observability

Identifiers reference

Tools

Steps — diagnose a failing AI agent

Steps — export automation history as CSV

Success criteria

Failure modes

See also

Bundled files

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Observability

Identifiers reference

Tools

Steps — diagnose a failing AI agent

Steps — export automation history as CSV

Success criteria

Failure modes

See also

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now