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pipefy-observability — Claude Code skill: agent logs and credit usage

A skill for checking Pipefy AI agent and automation logs, org usage stats, AI credit consumption, and exporting job history to CSV.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add pipefy/ai-toolkit --skill pipefy-observability -a claude-code
Open on GitHub

What pipefy-observability does

The skill gives your agent access to Pipefy observability: execution history for AI agents, automation logs, org-level usage stats, AI credit consumption, and export of automation job history. Inside are an identifier map for the tools and two step-by-step flows — diagnosing a failing AI agent and exporting history as CSV.

Agent logs need repo_uuid, the pipe UUID, not the numeric pipe_id. Pull the numeric ID through get_pipe first and take pipe.uuid from the response. Pass the numeric ID and get_ai_agent_logs comes back empty; that is the usual first-run mistake.

Export runs async: export_automation_jobs kicks off the job, get_automation_jobs_export polls status, get_automation_jobs_export_csv fetches the finished CSV. It advises waiting at least 60 seconds between polls. A finished export carries fileUrl, a signed link to the xlsx that expires 1 hour after the read that returned it — until then anyone holding it can download the file. If credits hit zero, AI features stop working until they are replenished, so flag it to the Pipefy admin.

What it helps with

  • find failed AI agent runs by pipe UUID
  • inspect a single agent execution detail
  • pull org-level automation execution stats
  • check remaining AI credits for the current month
  • export automation job history as CSV

Example prompts for the agent

  • show the last 30 agent logs for the pipe with UUID abc123
  • how many AI credits are left in organization 123 for current_month
  • trigger the automation jobs export and download the CSV

How to install pipefy-observability

1Skills CLI (Vercel)

npx skills add pipefy/ai-toolkit --skill pipefy-observability -a claude-code

2Manually: put the files in place

~/.claude/skills/pipefy-observability/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/pipefy-observability/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about pipefy-observability

How do I install pipefy-observability in Claude?

Run npx skills add pipefy/ai-toolkit --skill pipefy-observability -a claude-code. The skill lands in ~/.claude/skills/pipefy-observability/SKILL.md.

What does pipefy-observability do?

It reads Pipefy AI agent and automation logs, org-level usage stats and credit consumption, and exports automation job history as CSV.

Why does get_ai_agent_logs return an empty list?

Most likely a numeric pipe_id was passed instead of the pipe UUID. Grab pipe.uuid from get_pipe and retry.

Source text

SKILL.md · Apache-2.0
---
name: pipefy-observability
description: >
  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.
tags: [pipefy, observability, logs, usage, credits, exports]
---

# Observability

Read the [MCP reference](references/mcp.md) or [CLI reference](references/cli.md) 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](https://github.com/pipefy/ai-toolkit/blob/main/docs/mcp/tools/identifiers.md#observability).

| 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

1. **Get the pipe UUID** (not the numeric pipe ID):

   Operation: `get_pipe pipe_id=67890`

   Capture `pipe.uuid` from the response.

2. **Fetch recent agent logs:**

   Operation: `get_ai_agent_logs repo_uuid=<UUID> first=30`

3. **Identify the failed execution** — look for `status: failed` entries.

4. **Check credit usage** if the agent stopped unexpectedly:

   Operation: `get_ai_credit_usage organization_uuid=123 period="current_month"`

5. **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

1. **Trigger the export:**

   Operation: `export_automation_jobs organization_id=123 period="current_month"`

2. **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.

3. **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.

## See also

- `pipefy-ai-agents` — create and configure AI agents.
- `pipefy-automations` — create and debug automation rules.

© pipefy · Apache-2.0 · pipefy/ai-toolkit

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