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Thinking Pre Mortem

CommunityPopular
tjboudreaux
thinking-pre-mortem

Before committing to a plan or launch, assume it already failed and reason backward through concrete causes — convert failure paths into mitigations, gates, and stop checks.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-pre-mortem
Stars
1.3K
Forks
158
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by tjboudreaux on GitHub. Read the source before you install it.

Installation

Install the Thinking Pre Mortem AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tjboudreaux/cc-thinking-skills.git /tmp/cc-thinking-skills
mkdir -p .claude/skills
cp -r /tmp/cc-thinking-skills/skills/thinking-pre-mortem .claude/skills/thinking-pre-mortem
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Pre Mortem in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Thinking Pre Mortem on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Thinking Pre Mortem is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Pre-Mortem Analysis

Core rule: Prospective hindsight beats "what could go wrong?" Assume the plan has already failed (past tense), generate concrete failure paths, reverse them into prevention requirements, and bind each top path to a verifiable plan change.

When to Use

  • Before kickoff, major technical commitment, high-risk sprint work, launch, or release.
  • After a plan looks solid but before execution, especially when optimism or overconfidence is likely.
  • When a decision is costly to reverse and risks are still implicit rather than enforced.

When NOT to Use

  • Work is small, local, and cheaply reversible — skip the ceremony.
  • You are mid-incident under time pressure — act now (OODA / scientific method); pre-mortem is pre-execution.
  • You would only emit generic risks ("scope creep", "requirements unclear") that do not bind to this plan.
  • Risks are already enforced by live gates (CI, canary, rollback, alerts) with no residual unlisted path.
  • A mature risk register already covers the same specific failure paths with owners and checks.

Procedure

  1. Set the failure frame in past tense: "It is [post-deadline date]. The plan failed: [rollback / data loss / no adoption / SLA breach]." Explaining a happened failure is required; predicting one is not enough.
  2. Generate failure reasons before filtering: sweep technical, process, assumptions, dependencies/external, and people. Force a second pass: "What did the plan most need to be true that was not?" Collect breadth first (aim for many distinct reasons); do not rank mid-sweep.
  3. Prioritize: group by theme; score likelihood × impact; keep the top 3–5 concrete risks (drop generics that do not bind).
  4. Failure-first reverse analysis (absorbed reverse path): for each top risk, (a) restate the failed outcome in past tense, (b) list necessary/enabling conditions that had to be true for that failure, (c) invert each condition into a verifiable prevention requirement (avoid/require rule + check), (d) bind owner, verification checkpoint, and ship/stage gate.
  5. Mitigate and update the plan: for each top risk, attach mitigation steps, spike/contingency if needed, and the reverse-analysis prevention requirements as explicit plan gates. A pre-mortem with no plan change is incomplete.
  6. Stop when top risks each have a bound mitigation or prevention gate, or when further reasons are only generic restatements — then ship the updated plan, do not keep brainstorming.

Output

  1. Failure scenario — past-tense disaster frame with date and failed outcome.
  2. Failure reasons — unfiltered list, then prioritized top 3–5 with likelihood × impact.
  3. Reverse paths — for each top risk: necessary conditions → inverted prevention requirements.
  4. Mitigations — owner, verification checkpoint, ship/stage gate per top risk.
  5. Plan deltas — concrete tasks, spikes, contingencies, and gates added to the plan.

Verification

  • Falsify/stop: if no plan-specific failure path can be stated in past tense with necessary conditions, stop — you are generating theater, not risk. If a "mitigation" has no verification checkpoint, it is not done.
  • Over-application guard: do not pre-mortem reversible chores, mid-incident firefighting, or systems that already enforce the same paths. Do not leave abstract risks as "mitigate later"; either invert them into gates or drop them as non-actionable.

Frequently asked questions

What does the Thinking Pre Mortem AI skill do?

Before committing to a plan or launch, assume it already failed and reason backward through concrete causes — convert failure paths into mitigations, gates, and stop checks.

Why use Thinking Pre Mortem on TypingMind?

Because you install it once and use it with any model. Thinking Pre Mortem is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Thinking Pre Mortem in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tjboudreaux/cc-thinking-skills/tree/main/skills/thinking-pre-mortem. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Thinking Pre Mortem?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Thinking Pre Mortem?

As many as you like. As long as a model supports skills, you can use Thinking Pre Mortem with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Thinking Pre Mortem AI skill free?

Yes. It is published on GitHub by tjboudreaux under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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