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 aleksander-dytko/ai-pm-workspace --skill decisiongit clone --depth 1 https://github.com/aleksander-dytko/ai-pm-workspaceWrote 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/aleksander-dytko/ai-pm-workspace/decision)<a href="https://agentmods.dev/skills/aleksander-dytko/ai-pm-workspace/decision"><img src="https://agentmods.dev/badge/skills/aleksander-dytko/ai-pm-workspace/decision/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/aleksander-dytko/ai-pm-workspace/decision"><img src="https://agentmods.dev/badge/skills/aleksander-dytko/ai-pm-workspace/decision.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 | $0.00015 | $0.01201 |
| Opus 5.5 | $0.00006 | $0.00480 |
| Sonnet 5.5 | $0.00003 | $0.00240 |
| Haiku 4.5 | $0.00002 | $0.00120 |
Grade A, and why
decision 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 1mo 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Make a Product Decision
You help make a well-informed product decision by gathering context from all available sources and producing structured documentation, follow-up tasks, and a communication draft.
Input
The user provides a decision topic via $ARGUMENTS. Examples:
- "should we support feature X in the next release"
- "prioritize feature A vs feature B"
- "should we adopt GraphQL for the new API"
Workflow
-
Search the vault:
- Recent meetings in
Meetings/that touched this topic - Daily notes in
journals/(last 2 weeks) for related context Loose Notes/Work/for related decisions or draftsDashboard/Weekly P-Tasks.mdfor related priorities
- Recent meetings in
-
Use GitHub MCP (if configured):
- Search repositories for related issues/PRs
- Read engineering comments and discussions
- Identify any blockers or concerns raised
-
Ask for additional context (before options):
- "Do you have a Slack thread, document, or discussion about this decision? If yes, paste it now."
- If provided: extract opinions, concerns, constraints.
- If skipped: proceed without.
-
Present context summary:
- Relevant notes and prior discussions
- Strategy alignment (if strategy docs exist)
- Customer data or validation (if any)
- Engineering discussions and feasibility (from GitHub)
-
Draft options with trade-offs:
- 2-3 viable options
- For each: pros (benefits, alignment, customer value), cons (costs, risks, complexity, timeline)
- Do not recommend yet - present objectively
-
Create decision note:
- Location:
Loose Notes/Work/YYYY-MM-DD - Decision - [Topic].md - Structure:
- Location:
---
tags: LooseNotes
date: YYYY-MM-DD
---
# Decision: [Topic]
**Date**: YYYY-MM-DD
## Context
[Why this decision is needed - background from notes, meetings, strategy]
## Options Considered
1. **Option 1**: [Description]
- Pros: ...
- Cons: ...
2. **Option 2**: [Description]
- Pros: ...
- Cons: ...
## Decision
[To be filled after review, or suggest recommendation if clear]
## Rationale
[Why this option, trade-offs accepted]
## Impact
[What changes, who is affected, timeline]
## Communication
[Who needs to be informed, how]
## Follow-up Tasks
- [ ] [Task]
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.
- 1mo ago First seen · 138 lines · 15 tokens per session scan A ff2acd3b69f0
decision is a skill published in the GitHub repository aleksander-dytko/ai-pm-workspace (34 stars, last pushed 6mo ago), licensed MIT. It adds 15 tokens to every session and 1,201 once invoked, about $0.0001 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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