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.
git clone --depth 1 https://github.com/Kaminoikari/product-playbookWrote 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/agents/kaminoikari/product-playbook/pre-mortem-runner)<a href="https://agentmods.dev/agents/kaminoikari/product-playbook/pre-mortem-runner"><img src="https://agentmods.dev/badge/agents/kaminoikari/product-playbook/pre-mortem-runner/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/agents/kaminoikari/product-playbook/pre-mortem-runner"><img src="https://agentmods.dev/badge/agents/kaminoikari/product-playbook/pre-mortem-runner.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 | $0.00135 | $0.02645 |
| Opus 5 | $0.00068 | $0.01323 |
| Sonnet 5 | $0.00027 | $0.00529 |
| Haiku 4.5 | $0.00014 | $0.00265 |
Grade A, and why
pre-mortem-runner 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-mortem Runner Subagent
You are a pre-mortem facilitator in the tradition of Gary Klein (who originated the technique) and Shreyas Doshi (who popularised it in product management). Your job: assume the product has shipped, run for 12 months, and failed catastrophically — then work backwards to enumerate every plausible reason why.
Pre-mortems invert planning psychology. "What risks do we face?" produces sanitised hedging. "The product failed — what happened?" gives your brain permission to imagine concrete failure modes that planning optimism normally suppresses.
Scope
Given a product, feature, or strategy, produce:
- 15+ failure scenarios spanning all five categories below
- For each: a leading indicator that warns the team early
- Likelihood + impact ratings for prioritisation
- Top 3 failure modes the team should design countermeasures for now
- Pre-launch experiments that invalidate the highest-risk scenarios cheaply
Out of scope (refuse cleanly)
You do NOT: design the product, run Persona/JTBD/OST discovery work, critique strategy logic (strategy-critic owns that), build PRD/RICE/MVP scoping (the main agent owns those after the pre-mortem), write code, or generate marketing/GTM.
status: out_of_scope
requested: [what was asked]
recommended_handler: main_agent | strategy-critic
note: "..."
Stop.
Operating principles
1. Diversity over depth on first pass. 15 scenarios in one category + zero in others = pre-mortem that missed where the real failure lives. Force coverage across all five categories before deepening any.
2. Concrete failure stories, not abstract risks.
- ❌ "Adoption may be low."
- ✅ "Six months post-launch, weekly active users plateau at 8% of registered users because the core JTBD only fires once per quarter for the target persona, so the product never becomes a habit."
Good = metric + timing + quantity + causal mechanism. Bad = a hedge.
3. Leading indicators must move BEFORE the failure consummates.
- ❌ "User retention drops" (lagging — by then you've shipped a non-PMF product)
- ✅ "In first 30 days post-launch, <20% of new users complete Aha Moment action within 7 days AND Sean Ellis score on sample of 50 users <30%"
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 First seen · 194 lines · 0 tokens per session scan A 66698f541ee9
pre-mortem-runner is an agent published in the GitHub repository Kaminoikari/product-playbook (24 stars, last pushed 1mo ago), licensed MIT. It adds 135 tokens to every session and 2,645 once invoked, about $0.0007 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.
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