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 aniganti/pm-superpowers --skill pre-mortemgit clone --depth 1 https://github.com/aniganti/pm-superpowersWrote 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/aniganti/pm-superpowers/pre-mortem)<a href="https://agentmods.dev/skills/aniganti/pm-superpowers/pre-mortem"><img src="https://agentmods.dev/badge/skills/aniganti/pm-superpowers/pre-mortem/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/aniganti/pm-superpowers/pre-mortem"><img src="https://agentmods.dev/badge/skills/aniganti/pm-superpowers/pre-mortem.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00071 | $0.02018 |
| Opus 5.5 | $0.00028 | $0.00807 |
| Sonnet 5.5 | $0.00014 | $0.00404 |
| Haiku 4.5 | $0.00007 | $0.00202 |
Grade A, and why
pre-mortem 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-Mortem Risk Analysis
The goal of a pre-mortem is to improve launch readiness, not assign blame. You are helping the PM stress-test their product or launch plan by imagining failure before it happens — so the team can prevent it.
Process
1. Gather Context
Read the PRD or launch plan. The PM may provide:
- A file path to a PRD or launch plan document
- A URL to a document
- A verbal description of the product and launch
If no document is provided, ask the PM to describe:
- What is the product or feature?
- Who is the target user?
- What is the launch timeline?
- What does success look like (metrics, adoption targets, revenue goals)?
2. Research (If Relevant)
Use web search to research the competitive landscape, market conditions, or any external factors that could affect the launch. This is especially important if the product is entering a crowded market or a regulated industry.
3. Think Step by Step
Work through the following thought exercise carefully:
- Imagine the product launches in 14 days.
- Now imagine it fails — customers do not adopt, revenue targets miss, reputation takes a hit.
- What went wrong? What did we miss? What were we overconfident about?
Consider failure across multiple dimensions:
- Product quality and completeness
- User experience and onboarding
- Go-to-market and positioning
- Technical reliability and scalability
- Competitive response
- Internal alignment and resourcing
- Regulatory or compliance gaps
- Customer support readiness
- Data and analytics instrumentation
- Pricing and packaging
4. Categorize Risks
Sort every identified risk into one of three categories:
- Tigers — Real problems based on evidence, past experience, or clear logic. These should keep you up at night. They require action.
- Paper Tigers — Problems others might worry about, but are unlikely or overblown given the evidence. Worth documenting to align stakeholders and reduce anxiety. Not worth significant resource investment.
- Elephants — Unspoken concerns the team is not discussing enough. Could be real; uncertain. They deserve investigation before launch.
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 · 192 lines · 71 tokens per session scan A 5a1aa0dab8df
pre-mortem is a skill published in the GitHub repository aniganti/pm-superpowers (49 stars, last pushed 7d ago), licensed MIT. It adds 71 tokens to every session and 2,018 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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