pipefy-observability
This skill lets an agent monitor and troubleshoot Pipefy AI agents and automations by retrieving execution logs, usage statistics, credit consumption, and job…
Community author
- Agents
- Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode
- License
- Apache-2.0 (permissive)
- Pin
- 1960bbfpinned Oct 9, 2026
- Context cost
- about 1,100 tokenswhen the skill loads
- GitHub stars
- 49on pipefy/ai-toolkit
- Scripts
- None found
What it does
“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.”
Generated summary from the skill's own text
This skill lets an agent monitor and troubleshoot Pipefy AI agents and automations by retrieving execution logs, usage statistics, credit consumption, and job history. It provides read-only access to observability data across individual agents, automations, and organization-wide metrics, plus the ability to export automation job history as CSV.
When to use it
- Debugging why an AI agent failed or stopped executing
- Tracking AI credit usage and remaining balance across the organization
- Viewing execution metrics and trends for agents or automations
- Exporting automation job history for reporting or analysis
When not to
- Creating or configuring new agents or automations (use pipefy-ai-agents or pipefy-automations instead)
- Modifying agent or automation settings (this skill is read-only except exports)
Install
These commands install from the publisher's repository and need no account.
skills CLI
npx skills add pipefy/ai-toolkit --skill pipefy-observabilityInstalls from the repository's default branch, which may have changed since the commit we checked.
GitHub CLI 2.90 or later
gh skill install pipefy/ai-toolkit pipefy-observability@1960bbfec6f48dbe00b298108da5eef946c744e0Installs the commit we checked (1960bbf).
Copy by hand
Download the folder at 1960bbf and put it in .claude/skills/pipefy-observability for Claude Code and in .agents/skills/pipefy-observability for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode.
Copy for your agent
A prompt for any coding agent. It installs this skill from GitHub at the pinned commit, copies the files as they are, and runs nothing unless you ask.
Prompt
Install the agent skill "pipefy-observability" by pipefy. Source, pinned to commit 1960bbfec6f48dbe00b298108da5eef946c744e0: https://github.com/pipefy/ai-toolkit/tree/1960bbfec6f48dbe00b298108da5eef946c744e0/skills/observability/pipefy-observability 1. Download that folder at exactly this commit. Do not use the default branch. 2. Copy every file unchanged, keeping the folder structure, into .claude/skills/pipefy-observability/ for Claude Code and .agents/skills/pipefy-observability/ for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode. Create the folders if they do not exist. 3. Do not edit the files, and do not run any script in the folder unless I ask you to. 4. When you are done, list the files you copied and where you put them. Listing and checks: https://www.agentnative.dev/skills/pipefy/pipefy-observability
SKILL.md at the pin
--- 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.
From pipefy/ai-toolkit at 1960bbf, shown as plain text under its license: Apache-2.0 (permissive). The license text is in LICENSE.
View SKILL.md on GitHub at 1960bbfFiles
3 files, 6.4 KB in total. Each file has a sha256 in the pin's manifest; the first 12 characters are shown.
Skill folder on GitHub at 1960bbfRelated in Observability
debugging-executions
Debug workflow execution failures by inspecting failed nodes, re-running executions, and comparing expected versus actual values.
by n8nOfficial publisher
Claude Code, Codex, Cursor +3 · about 2.5K tokens · pinned 6 days ago
instance-awareness
This skill teaches the agent to resolve ambiguous requests by reading the instance's activity log and workflow history instead of asking the user to clarify.
by n8nOfficial publisher
Claude Code, Codex, Cursor +3 · about 1.3K tokens · pinned 6 days ago
creating-replay-vision-scanners
Guides agents through creating Replay Vision scanners, LLM probes that watch session recordings on a schedule and log findings as observations.
by PostHogOfficial publisher
Claude Code, Codex, Cursor +4 · about 3.3K tokens · pinned 6 days ago
exploring-replay-vision-observations
This skill guides agents through reading observations from Replay Vision scanners, understanding their findings across session recordings, and taking action…
by PostHogOfficial publisher
Claude Code, Codex, Cursor +4 · about 3.3K tokens · pinned 6 days ago
exploring-scouts
Explore and understand PostHog Signals scouts through read-only tools, examining fleet configuration, recent runs, performance metrics, and durable team…
by PostHogOfficial publisher
Contains scripts
Claude Code, Codex, Cursor +3 · about 11K tokens · pinned 6 days ago
investigating-replay
Gathers session recording metadata, user profile, event timeline, and linked error issues in parallel to give a complete investigation when a user provides a…
by PostHogOfficial publisher
Claude Code, Codex, Cursor +4 · about 2.3K tokens · pinned 6 days ago