Assumes a project has already failed, works backward to identify the top causes, scores them by likelihood and impact, and outputs a mitigation plan. Use when starting a new project, before a major launch, evaluating risky decisions, or when the user says "pre-mortem", "what could go wrong", "risk analysis", "failure modes", "red team this plan".
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Pre-Mortem
Assume the project has already failed. Work backward to find out why.
## Workflow
1. **Gather context** — Read the project description, plan, or doc the user provides. If a vault path is given, read it. If the user describes the project verbally, capture the key facts.
2. **Generate failure scenarios** — Imagine it is 6 months from now and the project has failed catastrophically. Generate 8-12 distinct failure causes across these categories:
- **Technical** — architecture gaps, scaling limits, dependency risks, security holes
- **Execution** — timeline, staffing, skill gaps, coordination failures
- **Market/External** — competition, regulation, user adoption, economic shifts
- **Organizational** — stakeholder alignment, funding, priority shifts, team morale
3. **Score each cause** — For each failure cause, assign:
- **Likelihood** (1-5): How probable is this failure mode?
- **Impact** (1-5): If it happens, how severe is the damage?
- **Risk Score** = Likelihood × Impact (1-25)
4. **Rank and filter** — Sort by risk score descending. Present the top 5-8.
5. **Mitigation plan** — For each top risk, provide:
…
The line at the top of this page pins the label git:20260628.9edbf07, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
A 17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (2641 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag
Source
GitHub
broomva/skills · 5 stars · license MIT · pushed 2026-11-01 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_aaqmmnia4lfxey4s
GET https://markdownregistry.com/api/v1/resolve?ref=broomva/skills/pre-mortem
GET https://markdownregistry.com/api/v1/blob/f02e3cfe00490ea724560985356dc3311c0e96f98b99a13f5ae452aef2802072
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
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