PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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 mohitagw15856/pm-claude-skills --skill ai-workflow-designergit clone --depth 1 https://github.com/mohitagw15856/pm-claude-skillsWrote 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/mohitagw15856/pm-claude-skills/ai-workflow-designer)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/ai-workflow-designer"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/ai-workflow-designer/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/mohitagw15856/pm-claude-skills/ai-workflow-designer"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/ai-workflow-designer.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00143 | $0.01137 |
| Opus 5.5 | $0.00057 | $0.00455 |
| Sonnet 5.5 | $0.00029 | $0.00227 |
| Haiku 4.5 | $0.00014 | $0.00114 |
Grade A, and why
ai-workflow-designer 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 2d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Workflow Designer
The mistake people make with AI is bolting it onto a task randomly — or trying to fully automate something that needs judgment, then losing trust when it goes wrong. Real leverage comes from designing the workflow: deciding which steps AI does well, which need a human, and where the checkpoints are. This maps that for your recurring task, so you get the speed of AI with the reliability of human judgment where it matters — and you stay in control.
What This Skill Produces
- The step map — the task broken into steps, each labeled: 🤖 AI does it · 🧑 human does it · ✅ human checks it (AI drafts, human approves)
- The right tool/prompt per AI step — what to use and how to prompt it for each automated step
- Hand-offs & checkpoints — how outputs pass between steps and where the human review points are (so errors are caught, not propagated)
- Failure modes & guards — where this workflow could go wrong (AI errors, hallucination, edge cases) and the checks that catch them
- A start-small rollout — how to introduce it incrementally and build trust before relying on it
- The keep-human line — the steps that should stay human (judgment, relationships, high-stakes calls) and why
Required Inputs
Ask for these if not provided:
- The task/process — the recurring thing you want AI to help with
- The current steps — how you do it now, manually
- The stakes — how much errors cost (drives how many human checkpoints)
- Your tools — the AI tools/access you have
- Your comfort — how much you want to automate vs. keep hands-on
Framework: Split The Steps, Check The Seams
- Map the current steps. Lay out how the task is done now — you can't design the AI version without seeing the manual one.
- Sort each step. For each: is it something AI does reliably (drafting, summarizing, extracting, transforming), something needing human judgment (decisions, relationships, high-stakes), or something AI drafts and a human approves?
- Pick tools and prompts. For each AI step, the right tool and a reliable prompt (often from a prompt library) — so the step works consistently.
- Design the seams. Where outputs hand off between steps is where errors hide — add review checkpoints at the seams, especially before anything irreversible or external-facing.
- Guard the failure modes. Name where AI could err (wrong facts, edge cases, confident nonsense) and the specific check that catches it before it matters.
- Roll out small. Start with the low-risk steps, verify the quality, and expand — building trust rather than automating everything and hoping.
- Keep humans where it counts. Be clear which steps should stay human — judgment, empathy, and high-stakes calls aren't candidates for automation.
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.
- 2d ago First seen · 73 lines · 143 tokens per session scan A 86f73d8ae791
ai-workflow-designer is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 143 tokens to every session and 1,137 once invoked, about $0.0006 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 2026-10-12.
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