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 skills add pipefy/ai-toolkit --skill pipefy-process-intelligencegit 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-process-intelligence)<a href="https://agentmods.dev/skills/pipefy/ai-toolkit/pipefy-process-intelligence"><img src="https://agentmods.dev/badge/skills/pipefy/ai-toolkit/pipefy-process-intelligence/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pipefy/ai-toolkit/pipefy-process-intelligence"><img src="https://agentmods.dev/badge/skills/pipefy/ai-toolkit/pipefy-process-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
- Socket pass
- Snyk warn
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 5.1 | $0.00065 | $0.01669 |
| Opus 5.5 | $0.00026 | $0.00668 |
| Sonnet 5.5 | $0.00013 | $0.00334 |
| Haiku 4.5 | $0.00006 | $0.00167 |
Grade A, and why
pipefy-process-intelligence 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 11d 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Process Intelligence
Read the MCP reference or CLI reference for the surface you are using. Load only the relevant reference.
Analyze existing pipes for improvement opportunities and implement them progressively. Investigate immediately. Diagnose with data. Improve progressively.
When to use
The user asks to analyze or improve an existing process:
- "Analyze my pipe"
- "How can I improve this process?"
- "Is this pipe optimized?"
- "Where are the bottlenecks?"
Not for: designing a new process from scratch → use pipefy-process-design. Justifying a change with numbers, without implementing it → use pipefy-process-impact.
Prerequisites
- Pipe ID or name is known (or searchable via
search_pipes). - Read access to the pipe's cards and phase data.
Steps — investigation (Round 1)
-
Get pipe structure:
Operation:
get_pipe pipe_id=<id>Capture: phases, field count per phase, automation count.
-
Sample recent cards (last 30–50):
Operation:
get_cards pipe_id=<id> first=50 include_fields=trueLook for: cards piling up in early phases, phases with 0 cards, fields left empty.
get_cardsselects no creation, update, or phase timestamps; the timed card read under the diagnosis framework does. -
Check automations:
Operation:
get_automations pipe_id=<id>Look for: phases with no automations (manual handoffs), repeated manual steps.
-
Check AI configuration:
Operation:
get_ai_agents repo_uuid=<PIPE_UUID>For each active agent (
disabledAtis null), read its behaviors to see which phases it already acts on:Operation:
get_ai_agent uuid=<AGENT_UUID>Look for: no AI agents despite manual categorization or triage patterns, and hops an active behavior already covers (do not report those as manual).
Diagnosis framework
Time-based signals need dates that get_pipe and get_cards do not return. Per-card signals (stuck in a phase for more than 7 days, no update in weeks) come from a timed card read: one execute_graphql query on the pipe's cards selecting updated_at, current_phase_age, and phases_history (firstTimeIn, lastTimeOut, duration in seconds per phase); the document is in the reference. It is a read with no confirmation, 25 cards per page (up to 50), so say how many cards the numbers come from.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago Changed · +16 lines 4dc3f1667e75
- 1mo ago First seen · 137 lines · 65 tokens per session scan A 8d5b70133bf7
pipefy-process-intelligence is a skill published in the GitHub repository pipefy/ai-toolkit (50 stars, last pushed yesterday), licensed Apache-2.0. It adds 65 tokens to every session and 1,669 once invoked, about $0.0003 per session on Opus 5.5. 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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