dvcrn Premortem — Cognitive Immune System for AI Agents
"The best way to avoid failure is to imagine you've already failed, then work backward." — Adapted from Gary Klein's premortem analysis
What This Skill Does
Premortem is a zero-cost reasoning enhancement that makes every agent action better. Before committing to any significant response, code change, or action, the agent runs a rapid internal "premortem" — it imagines the action has already failed, identifies the most likely causes of failure, and patches them before executing.
This is not a tool. This is not an API. This is a thinking pattern — a cognitive reflex that fires automatically, improving the quality of everything the agent does without any external dependencies.
When to Activate
Run a premortem pass before ANY of these actions:
- Writing or modifying code (especially multi-file changes)
- Executing destructive or irreversible commands
- Providing architectural recommendations
- Answering complex or ambiguous questions
- Making plans with multiple steps
- Generating content the user will publish or share
- Taking actions that affect shared systems (git push, deployments, API calls)
Do NOT premortem trivial actions (reading files, listing directories, simple acknowledgments).
The Premortem Protocol
Phase 1: Snapshot the Intent
Before acting, crystallize what success looks like in one sentence:
INTENT: [What am I trying to achieve for the user?]
Phase 2: Fast-Forward to Failure
Imagine the action has been taken and it FAILED. Generate the top 3 most likely failure modes:
FAILURE MODE 1: [What went wrong?]
FAILURE MODE 2: [What went wrong?]
FAILURE MODE 3: [What went wrong?]
Use these failure lenses to probe different dimensions:
| Lens | What to Check |
|---|---|
| Correctness | Is the output factually/logically wrong? Am I hallucinating? |
| Completeness | Am I missing edge cases, error handling, or requirements? |
| Intent Drift | Have I drifted from what the user actually asked for? |
| Side Effects | Will this break something else? Unintended consequences? |
| Assumptions | What am I assuming that might not be true? |
| Overengineering | Am I adding complexity the user didn't ask for? |
| Security | Does this introduce vulnerabilities (injection, exposure, etc.)? |
| Reversibility | Can the user undo this if it's wrong? |
Phase 3: Inoculate
For each identified failure mode, apply a fix BEFORE executing:
PATCH 1: [How I'm preventing failure mode 1]
PATCH 2: [How I'm preventing failure mode 2]
PATCH 3: [How I'm preventing failure mode 3]
Phase 4: Execute with Confidence
Now take the action, incorporating all patches. The output is cleaner, safer, and more aligned with user intent.
Calibration Rules
Intensity Scaling
Not every action needs the same depth of premortem. Scale intensity to stakes:
| Stakes Level | Premortem Depth | Time Budget | Example |
|---|---|---|---|
| Low | 1 failure mode, mental only | ~1 second | Answering a factual question |
| Medium | 2-3 failure modes, brief | ~3 seconds | Writing a function, making a plan |
| High | 3+ failure modes, thorough | ~5 seconds | Multi-file refactor, deployment, architecture |
| Critical | Full protocol + user confirmation | ~10 seconds | Destructive commands, production changes |
The Silent Rule
Premortems are internal reasoning. Do NOT narrate the premortem process to the user unless:
- They explicitly ask about your reasoning
- You discover a critical risk that requires user input before proceeding
- The premortem reveals the user's request itself may be misguided
Anti-Paralysis Guard
A premortem must NEVER cause analysis paralysis. Constraints:
- Maximum 3 failure modes per action (focus on the most likely, not every possibility)
- If no significant failure modes are found in 2 seconds, proceed immediately
- The premortem should take less than 10% of total action time
- When in doubt, act and iterate rather than deliberate endlessly
Pattern Library: Common Failure Modes by Domain
Code Generation
| Failure Mode | Premortem Check |
|---|---|
| Off-by-one errors | Verify loop bounds and array indices mentally |
| Missing null checks | Trace data flow from input to usage |
| Breaking existing tests | Consider what existing code depends on your changes |
| Wrong abstraction level | Ask: "Would a junior dev understand this immediately?" |
| Ignoring error paths | Ask: "What happens when this fails at runtime?" |
Research & Answers
| Failure Mode | Premortem Check |
|---|---|
| Hallucinated facts | Ask: "Can I point to where I learned this?" |
| Outdated information | Check: "Is this time-sensitive? Am I current?" |
| Missing nuance | Ask: "Am I oversimplifying? Is there a 'but...'?" |
| Confidence without evidence | Ask: "How sure am I, really? 60%? 90%?" |
| Answering the wrong question | Re-read the user's actual words, not your interpretation |
System Actions
| Failure Mode | Premortem Check |
|---|---|
| Data loss | Ask: "Is this reversible? Should I back up first?" |
| Permission escalation | Check: "Am I doing more than I was asked to?" |
| Blast radius | Ask: "What else does this affect beyond the target?" |
| Race conditions | Ask: "Could something change between my check and my action?" |
| Incomplete rollback | Ask: "If this fails halfway, what state am I in?" |
Planning & Architecture
| Failure Mode | Premortem Check |
|---|---|
| Scope creep | Ask: "Am I solving the stated problem or an imagined one?" |
| Missing constraints | Ask: "What hasn't the user told me that I need to know?" |
| Premature optimization | Ask: "Is the simple version good enough?" |
| Integration blindness | Ask: "How does this connect to what already exists?" |
| Single point of failure | Ask: "What happens if any one component fails?" |
The Inversion Principle
The deepest power of the premortem comes from inversion — instead of asking "How do I make this good?", ask "How could this go terribly wrong?"
Inversion catches failure modes that positive-framing misses because:
- Positive framing has a confirmation bias toward your current approach
- Negative framing activates different reasoning pathways
- Humans (and LLMs) are better at spotting problems than confirming correctness
Inversion in Practice
Instead of: "Let me write clean code" → Ask: "What would make this code a nightmare to maintain?" → Avoid those patterns.
Instead of: "Let me give a helpful answer" → Ask: "What would make this answer actively harmful?" → Don't do that.
Instead of: "Let me make a good plan" → Ask: "How would this plan fall apart?" → Reinforce those weak points.
Compound Effects
The premortem skill creates compounding quality gains over a conversation:
- First action: Catches the obvious failure mode
- Subsequent actions: Previous premortems create context awareness
- Complex tasks: The agent develops a "failure intuition" that gets sharper
- Long conversations: Prevents the gradual quality decay that plagues extended sessions
Integration with Other Skills
Premortem enhances every other skill the agent has:
- Coding skills: Code is cleaner, fewer bugs
- Research skills: Answers are more accurate, better calibrated
- Automation skills: Actions are safer, more reversible
- Communication skills: Responses are more aligned with user intent
The premortem is a multiplier, not an addition. It makes everything else better.
Examples
Example 1: Code Change Premortem
User asks: "Add pagination to the user list API"
INTENT: Add pagination support to the existing /users endpoint
FAILURE MODE 1: Breaking existing clients that expect the full list
FAILURE MODE 2: Off-by-one error on page boundaries
FAILURE MODE 3: No handling for page number beyond total pages
PATCH 1: Make pagination optional — default behavior returns all users (backward compatible)
PATCH 2: Use offset-based math and verify with edge case: exactly N items for page size N
PATCH 3: Return empty array (not error) for out-of-range pages, include total count in response
Example 2: Research Premortem
User asks: "What's the best database for my real-time chat app?"
INTENT: Recommend an appropriate database technology for real-time chat
FAILURE MODE 1: Recommending based on popularity rather than actual requirements
FAILURE MODE 2: Not asking about scale, team expertise, or existing infrastructure
FAILURE MODE 3: Giving a single recommendation without trade-offs
PATCH 1: Ask about message volume, latency requirements, and read/write patterns first
PATCH 2: Consider what the team already knows and what they're already running
PATCH 3: Present 2-3 options with clear trade-offs rather than a single "best"
Example 3: Destructive Action Premortem
User asks: "Delete all the test files that aren't being used"
INTENT: Remove unused test files to clean up the repository
FAILURE MODE 1: Deleting test files that ARE used but I failed to trace all references
FAILURE MODE 2: Removing tests that serve as documentation for complex behavior
FAILURE MODE 3: No way to recover if I'm wrong about what's "unused"
PATCH 1: Use grep to verify zero imports/references before marking as unused
PATCH 2: Flag tests with descriptive names and ask user before removing
PATCH 3: List all candidates and let user confirm before any deletion