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 agentmods add skills/pandysp/claude-plugins/pre-mortemnpx skills add pandysp/claude-plugins --skill pre-mortemgit clone --depth 1 https://github.com/pandysp/claude-pluginsWrote 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/pandysp/claude-plugins/pre-mortem)<a href="https://agentmods.dev/skills/pandysp/claude-plugins/pre-mortem"><img src="https://agentmods.dev/badge/skills/pandysp/claude-plugins/pre-mortem.svg" alt="Measured on agentmods" 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 | $0.00096 | $0.00859 |
| Opus 5 | $0.00048 | $0.00430 |
| Sonnet 5 | $0.00019 | $0.00172 |
| Haiku 4.5 | $0.00010 | $0.00086 |
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 4d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-mortem: assume it failed, work backward
Imagine this has already failed. What went wrong?
The pre-mortem inverts the usual optimistic framing. Instead of asking "will this work?" (which biases toward yes), assume it already failed and work backward to figure out why. Surfaces risks that optimism would hide.
Process
1. Set the scene
State what's being evaluated, what success looks like, the timeline, and the blast radius.
2. Imagine failure
Assume it's 3 months later and this failed. Generate failure scenarios across these categories. They're prompts, not a checklist.
- Mechanism: what breaks under load, scale, or real-world data? Dependency changes? Network slow, disk full, API rate-limits?
- Structure: where does the design leak? What requirements emerge that this can't accommodate? Where is it optimized for today and fragile against tomorrow?
- Boundary: what happens at edges with other systems? Upstream contract changes? Data format evolves?
- Human: what if the next developer misunderstands the design? What if someone uses it in a way you didn't anticipate? Will error messages actually help?
- Operations: how do you know this is working in production? What's observable? What happens when it fails silently? How do you roll back?
After scanning these 5, ask: what doesn't fit any of these that I should worry about? Name it as a 6th. The most dangerous failures are often the ones that don't fit the boxes.
3. Rank by danger
For each failure mode, assess:
- Likelihood: how probable? (not "could it happen" but "will it happen given enough time")
- Impact: recoverable annoyance or catastrophic data loss?
- Detectability: would you know it happened? Silent failures are worse than loud ones.
The most dangerous failures are likely + high-impact + hard to detect. Lead with those.
4. Identify detection strategies
For each significant failure mode: how would you know if it happened? What metric, log line, user behavior, or test would reveal it?
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.
- 4d ago First seen · 66 lines · 96 tokens per session scan A e8f4184e3411
pre-mortem is a skill published in the GitHub repository pandysp/claude-plugins (5 stars, last pushed 13d ago), licensed MIT. It adds 96 tokens to every session and 859 once invoked, about $0.0005 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 2026-08-31.
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chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
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