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/pipefy/ai-toolkit/pipefy-observabilitynpx skills add pipefy/ai-toolkit --skill pipefy-observabilitygit clone --depth 1 https://github.com/pipefy/ai-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pipefy/ai-toolkit/pipefy-observability)<a href="https://agentmods.dev/skills/pipefy/ai-toolkit/pipefy-observability"><img src="https://agentmods.dev/badge/skills/pipefy/ai-toolkit/pipefy-observability.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00044 | $0.01078 |
| Opus 5 | $0.00022 | $0.00539 |
| Sonnet 5 | $0.00009 | $0.00216 |
| Haiku 4.5 | $0.00004 | $0.00108 |
Grade A, and why
pipefy-observability 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 5d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability
Monitor AI agent and automation execution, usage stats, credit consumption, and export job history. 11 MCP tools.
Identifiers reference
Full cross-tool map: 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
| Tool (MCP) | CLI | Read-only | Purpose |
|---|---|---|---|
get_ai_agent_logs |
pipefy agent logs list |
Yes | Execution history for a specific AI agent. |
get_ai_agent_log_details |
pipefy agent logs get |
Yes | Single execution detail for an AI agent log entry. |
get_automation_logs |
pipefy automation logs --automation |
Yes | Execution history for an automation (by automation ID). |
get_automation_logs_by_repo |
pipefy automation logs --repo |
Yes | Automation logs filtered by pipe. |
get_agents_usage |
pipefy usage agents |
Yes | Org-level AI agent execution count and trends. |
get_automations_usage |
pipefy usage automations |
Yes | Org-level automation execution stats. |
get_automation_execution_metrics |
pipefy usage execution-metrics |
Yes | Per-automation execution metrics (totalRuns, success/failure rate, avg duration, lastRun) over a rolling window; partial success returns partial_errors for denied ids. |
get_ai_credit_usage |
pipefy usage credits |
Yes | AI credit consumption and remaining balance. |
export_automation_jobs |
pipefy export automation-jobs |
Yes | Trigger async export of automation job history. |
get_automation_jobs_export |
pipefy automation export status |
Yes | Poll export job status (after export_automation_jobs). |
get_automation_jobs_export_csv |
pipefy export automation-jobs-csv |
Yes | Download finished automation-jobs export as CSV text. |
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.
- 5d ago First seen · 104 lines · 44 tokens per session scan A ae5136862e14
pipefy-observability is a skill published in the GitHub repository pipefy/ai-toolkit (45 stars, last pushed yesterday), licensed Apache-2.0. It adds 44 tokens to every session and 1,078 once invoked, about $0.0002 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 2026-08-30.
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