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 WaterplanAI/agentic-config --skill ac-workflow-product-managergit clone --depth 1 https://github.com/WaterplanAI/agentic-configWrote 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/waterplanai/agentic-config/ac-workflow-product-manager)<a href="https://agentmods.dev/skills/waterplanai/agentic-config/ac-workflow-product-manager"><img src="https://agentmods.dev/badge/skills/waterplanai/agentic-config/ac-workflow-product-manager/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/waterplanai/agentic-config/ac-workflow-product-manager"><img src="https://agentmods.dev/badge/skills/waterplanai/agentic-config/ac-workflow-product-manager.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 | $0.00053 | $0.02624 |
| Opus 5 | $0.00026 | $0.01312 |
| Sonnet 5 | $0.00011 | $0.00525 |
| Haiku 4.5 | $0.00005 | $0.00262 |
Grade A, and why
ac-workflow-product-manager 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 10d 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 — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Manager - Phase Decomposition
Decomposes large features/bugs/chores into concrete, self-contained development phases.
Core Principles
- Self-Contained Phases: Each phase produces fully functional code (even if partial to overall goal)
- Incremental Value: Every phase delivers testable, deployable value
- Clear Boundaries: Phases have explicit inputs, outputs, and acceptance criteria
- DAG Dependencies: Phases form a directed acyclic graph - parallel where possible
Input Analysis
When given a feature/prompt/spec:
- Read input thoroughly - Understand full scope
- Identify atomic units - Find smallest deployable pieces
- Map dependencies - What must exist before what
- Group logically - Combine related atoms into phases
- Validate DAG - Ensure no circular dependencies
Phase Decomposition Rules
Phase Sizing
- Too small: Single file change, trivial addition
- Just right: 1-3 related components, clear acceptance criteria, testable
- Too large: Multiple unrelated concerns, can't be tested independently
Feature Complexity Assessment
Assess overall feature complexity to guide phase count and downstream execution-modifier selection:
| Feature Complexity | Typical Phases | Default Phase Modifier |
|---|---|---|
| Simple | 1-2 | lean or leanest |
| Medium | 2-4 | lean or normal |
| Complex | 4-8 | normal or full |
| Very Complex | 8+ | full (critical phases) |
Phase Independence
Each phase MUST:
- Have clear entry point (what exists before)
- Produce working code (no broken intermediate states)
- Be testable in isolation
- Have explicit acceptance criteria
Dependency Types
hard: Phase cannot start until dependency completessoft: Phase benefits from dependency but can proceed with stubs
Output Format
Complexity to downstream execution mapping
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.
- 10d ago First seen · 334 lines · 53 tokens per session scan A 8324cd4f4a10
ac-workflow-product-manager is a skill published in the GitHub repository WaterplanAI/agentic-config (30 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 2,624 once invoked, about $0.0003 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.
Other skills, from other repositories
vfx-text-cursor
A video or hero-frame effect where a cursor reveals a sentence character by character with coloured trails, light streaks, and a final shimmer.
migrate-skills-between-libraries
Use when moving skills between library workspaces or upgrading from a personal library to a team library. Export from one workspace, import into another.
review-a-skill
Use when evaluating whether a skill belongs in a library. Preview content, check frontmatter, validate structure, and decide whether to keep, curate, or remove.
audit-library-health
Use when checking the overall health of a skills library. Run doctor, validate, check for stale skills, and verify generated docs are in sync.
browse-and-evaluate
Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.
install-from-remote-library
Use when installing skills from a shared ai-agent-skills library repo. Inspect with --list first, prefer --collection, and preview with --dry-run before installing.