Agent skill

Premortem

by parcadei in parcadei/Continuous-Claude-v3

Identify failure modes before they occur using structured risk analysis

MITAuto-check passed

Install Premortem

skills CLI
$ npx skills add parcadei/Continuous-Claude-v3 --skill premortem -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install parcadei/Continuous-Claude-v3 premortem --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/premortem .claude/skills/premortem && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
premortem
GitHub stars
3.9k
Used in
1 other repo
Token cost
~3.1k tokens
SKILL.md length
459 words
Files
1
Skills in repo
141
Repo updated
First seen
Licence
MIT

At a glance

Identify failure modes before they occur using structured risk analysis

  • Works in 5 steps: Detect Context & Depth → Run Appropriate Checklist → Present Risks via AskUserQuestion → …
  • SKILL.md covers Usage, Core Concept, Risk Categories (Shreyas… and CRITICAL: Verify Before Flagging, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Premortem is an agent skill from parcadei/Continuous-Claude-v3. Identify failure modes before they occur using structured risk analysis

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Context management for Claude Code. Hooks maintain state via ledgers and handoffs. MCP execution without context pollution. Agent orchestration with isolated context windows. The licence is MIT.

Example prompts

  • “/premortem”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Grep, Glob, Task, AskUserQuestion, TodoWrite

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Detect Context & Depth
  2. Run Appropriate Checklist
  3. Present Risks via AskUserQuestion
  4. Handle User Response
  5. Update Plan (if mitigations added)

What it can do on your machine

Read from SKILL.md and the folder at commit d07ff4b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Grep
    • Glob
    • Task
    • AskUserQuestion
    • TodoWrite

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python, yaml and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • coda.io
    • hbr.org
    • mountaingoatsoftware.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Premortem loads about 3.1k tokens when it runs. Until then it costs about 20 tokens; SKILL.md has 459 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~20
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from parcadei/Continuous-Claude-v3 at commit d07ff4b, republished under its MIT licence (© parcadei). 459 words, ~3,070 tokens.

Download SKILL.mdSave it as .claude/skills/premortem/SKILL.md (or your agent's skills folder).
name
premortem
description
Identify failure modes before they occur using structured risk analysis
allowed-tools
Read, Grep, Glob, Task, AskUserQuestion, TodoWrite

Pre-Mortem

Identify failure modes before they occur by systematically questioning plans, designs, and implementations. Based on Gary Klein's technique, popularized by Shreyas Doshi (Stripe).

Usage

/premortem              # Auto-detect context, choose depth
/premortem quick        # Force quick analysis (plans, PRs)
/premortem deep         # Force deep analysis (before implementation)
/premortem <file>       # Analyze specific plan or code

Core Concept

"Imagine it's 3 months from now and this project has failed spectacularly. Why did it fail?"

Risk Categories (Shreyas Framework)

CategorySymbolMeaning
Tiger[TIGER]Clear threat that will hurt us if not addressed
Paper Tiger[PAPER]Looks threatening but probably fine
Elephant[ELEPHANT]Thing nobody wants to talk about

CRITICAL: Verify Before Flagging

Do NOT flag risks based on pattern-matching alone. Every potential tiger MUST go through verification.

The False Positive Problem

Common mistakes that create false tigers:

  • Seeing a hardcoded path without checking for if exists(): fallback
  • Finding missing feature X without asking "is X in scope?"
  • Flagging code at line N without reading lines N±20 for context
  • Assuming error case isn't handled without tracing the code
Verification Checklist (REQUIRED)

Before flagging ANY tiger, verify:

yaml
potential_finding:
  what: "Hardcoded path at line 42"

verification:
  context_read: true    # Did I read ±20 lines around the finding?
  fallback_check: true  # Is there try/except, if exists(), or else branch?
  scope_check: true     # Is this even in scope for this code?
  dev_only_check: true  # Is this in __main__, tests/, or dev-only code?

result: tiger | paper_tiger | false_alarm

If ANY verification check is "no" or "unknown", DO NOT flag as tiger.

Required Evidence Format

Every tiger MUST include:

yaml
tiger:
  risk: "<description>"
  location: "file.py:42"
  severity: high|medium
  # REQUIRED - what mitigation was checked and NOT found:
  mitigation_checked: "No exists() check, no try/except, no fallback branch"

If you cannot fill in mitigation_checked with specific evidence, it's not a verified tiger.

Workflow

Step 1: Detect Context & Depth
python
# Auto-detect based on context
if in_plan_creation:
    depth = "quick"   # Localized scope
elif before_implementation:
    depth = "deep"    # Global scope
elif pr_review:
    depth = "quick"   # Localized scope
else:
    # Ask user
    AskUserQuestion(
        question="What depth of pre-mortem analysis?",
        header="Depth",
        options=[
            {"label": "Quick (2-3 min)", "description": "Plans, PRs, localized changes"},
            {"label": "Deep (5-10 min)", "description": "Before implementation, global scope"}
        ]
    )
Step 2: Run Appropriate Checklist
Quick Checklist (Plans, PRs)

Run through these mentally, note any that apply:

Core Questions:

  1. What's the single biggest thing that could go wrong?
  2. Any external dependencies that could fail?
  3. Is rollback possible if this breaks?
  4. Edge cases not covered in tests?
  5. Unclear requirements that could cause rework?

Output Format:

yaml
premortem:
  mode: quick
  context: "<plan/PR being analyzed>"

  # Two-pass process: first gather potential risks, then verify each one
  potential_risks:  # Pass 1: Pattern-matching findings
    - "hardcoded path at line 42"
    - "missing error handling for X"

  # Pass 2: After verification
  tigers:
    - risk: "<description>"
      location: "file.py:42"
      severity: high|medium
      category: dependency|integration|requirements|testing
      mitigation_checked: "<what was NOT found>"  # REQUIRED

  elephants:
    - risk: "<unspoken concern>"
      severity: medium

  paper_tigers:
    - risk: "<looks scary but ok>"
      reason: "<why it's fine - what mitigation EXISTS>"
      location: "file.py:42-48"  # Show the mitigation location

  false_alarms:  # Findings that turned out to be nothing
    - finding: "<what was initially flagged>"
      reason: "<why it's not a risk>"
Show full SKILL.md (206 more words)Show less
Deep Checklist (Before Implementation)

Work through each category systematically:

Technical Risks:

  • Scalability: Works at 10x/100x current load?
  • Dependencies: External services + fallbacks defined?
  • Data: Availability, consistency, migrations clear?
  • Latency: SLA requirements will be met?
  • Security: Auth, injection, OWASP considered?
  • Error handling: All failure modes covered?

Integration Risks:

  • Breaking changes identified?
  • Migration path defined?
  • Rollback strategy exists?
  • Feature flags needed?

Process Risks:

  • Requirements clear and complete?
  • All stakeholder input gathered?
  • Tech debt being tracked?
  • Maintenance burden understood?

Testing Risks:

  • Coverage gaps identified?
  • Integration test plan exists?
  • Load testing needed?
  • Manual testing plan defined?

Output Format:

yaml
premortem:
  mode: deep
  context: "<implementation being analyzed>"

  # Two-pass process
  potential_risks:  # Pass 1: Initial scan findings
    - "no circuit breaker for external API"
    - "hardcoded timeout value"

  # Pass 2: After verification (read context, check for mitigations)
  tigers:
    - risk: "<description>"
      location: "file.py:42"
      severity: high|medium
      category: scalability|dependency|data|security|integration|testing
      mitigation_checked: "<what mitigations were looked for and NOT found>"
      suggested_fix: "<how to address>"

  elephants:
    - risk: "<unspoken concern>"
      severity: medium|high
      suggested_fix: "<suggested approach>"

  paper_tigers:
    - risk: "<looks scary>"
      reason: "<why it's actually ok - cite the mitigation code>"
      location: "file.py:45-52"

  false_alarms:
    - finding: "<initial concern>"
      reason: "<why verification showed it's not a risk>"

  checklist_gaps:
    - category: "<which checklist section>"
      items_failed: ["<item1>", "<item2>"]
Step 3: Present Risks via AskUserQuestion

BLOCKING: Present findings and require user decision.

python
# Build risk summary
risk_summary = format_risks(tigers, elephants)

AskUserQuestion(
    question=f"""Pre-Mortem identified {len(tigers)} tigers, {len(elephants)} elephants:

{risk_summary}

How would you like to proceed?""",
    header="Risks",
    options=[
        {
            "label": "Accept risks and proceed",
            "description": "Acknowledged but not blocking"
        },
        {
            "label": "Add mitigations to plan (Recommended)",
            "description": "Update plan with risk mitigations before proceeding"
        },
        {
            "label": "Research mitigation options",
            "description": "I don't know how to mitigate - help me find solutions"
        },
        {
            "label": "Discuss specific risks",
            "description": "Talk through particular concerns"
        }
    ]
)
Step 4: Handle User Response
If "Accept risks and proceed"
python
# Log acceptance for audit trail
print("Risks acknowledged. Proceeding with implementation.")
# Continue to next workflow step
If "Add mitigations to plan"
python
# User provides mitigation approach
# Update plan file with mitigations section
# Re-run quick premortem to verify mitigations address risks
If "Research mitigation options"
python
# Spawn parallel research for each HIGH severity tiger
for tiger in high_severity_tigers:
    # Internal: How has codebase handled this before?
    Task(
        subagent_type="scout",
        prompt=f"""
        Find how this codebase has previously handled: {tiger.category}

        Specifically looking for patterns related to: {tiger.risk}

        Return:
        - File:line references to similar solutions
        - Patterns used
        - Libraries/utilities available
        """
    )

    # External: What are best practices?
    Task(
        subagent_type="oracle",
        prompt=f"""
        Research best practices for: {tiger.risk}

        Context: {tiger.category} in a {tech_stack} codebase

        Return:
        - Recommended approaches (ranked)
        - Library options
        - Common pitfalls to avoid
        """
    )

# Wait for research to complete
# Synthesize options
# Present via AskUserQuestion with 2-4 mitigation options
If "Discuss specific risks"
python
# Ask which risk to discuss
AskUserQuestion(
    question="Which risk would you like to discuss?",
    header="Risk",
    options=[format_risk_option(r) for r in all_risks[:4]]
)
# Then have conversation about that specific risk
Step 5: Update Plan (if mitigations added)

If user added mitigations, append to the plan:

markdown
## Risk Mitigations (Pre-Mortem)

### Tigers Addressed:
1. **{risk}** (severity: {severity})
   - Mitigation: {user_or_researched_mitigation}
   - Added to phase: {phase_number}

### Accepted Risks:
1. **{risk}** - Accepted because: {reason}

### Pre-Mortem Run:
- Date: {timestamp}
- Mode: {quick|deep}
- Tigers: {count}
- Elephants: {count}

Integration Points

In create_plan / plan-agent

After plan structure is approved, before ExitPlanMode:

python
# Run quick premortem
/premortem quick

# If HIGH risks found, block until addressed
# If only MEDIUM/LOW, inform and proceed
After plan approval, before implementation
python
# Run deep premortem on full plan
/premortem deep thoughts/shared/plans/YYYY-MM-DD-feature.md

# Block until all HIGH tigers addressed
In PR review workflows
python
# Run quick premortem on diff scope
/premortem quick

# Inform of any risks found

Severity Thresholds

SeverityBlocking?Action Required
HIGHYesMust address or explicitly accept
MEDIUMNoInform user, recommend addressing
LOWNoNote for awareness

Example Session

User: /premortem deep thoughts/shared/plans/2026-01-08-api-rate-limiting.md

Claude: Running deep pre-mortem on API rate limiting plan...

[Reviews plan against deep checklist]

Pre-mortem complete. Found 2 tigers, 1 elephant:

**TIGERS:**
1. [HIGH] No circuit breaker for external payment API
   - Category: dependency
   - If payment API is slow/down, requests will pile up

2. [HIGH] No rollback strategy defined
   - Category: integration
   - If rate limiting breaks auth flow, no quick fix path

**ELEPHANTS:**
1. [MEDIUM] Team hasn't used Redis before
   - We're introducing Redis for rate limit counters
   - Learning curve could slow implementation

**PAPER TIGERS:**
1. Database migration size - Only adds one index, <1s migration

[AskUserQuestion presented]

User: Research mitigation options

Claude: Researching mitigations...

[Spawns scout + oracle in parallel]

Found 3 options for circuit breaker:

1. **Use existing retry util** (Recommended)
   - Found at src/utils/retry.ts:45
   - Already handles exponential backoff
   - Just need to add circuit breaker state

2. **Add resilience4j-lite**
   - Popular library for this pattern
   - More features but new dependency

3. **Custom implementation**
   - Full control but more code to maintain

Which approach for the circuit breaker?

User: Option 1 - use existing retry util

Claude: Added to plan:

## Risk Mitigations (Pre-Mortem)

### Tigers Addressed:
1. **No circuit breaker** (HIGH)
   - Mitigation: Extend src/utils/retry.ts with circuit breaker state
   - Added to Phase 1

Shall I also address the rollback strategy tiger?

References

© parcadei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/premortem of parcadei/Continuous-Claude-v3.

Open the folder on GitHubat commit d07ff4b

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in parcadei/Continuous-Claude-v3, which our catalogue first saw on October 7, 2026.

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Questions about Premortem

What does Premortem do?

Identify failure modes before they occur using structured risk analysis. Premortem is an agent skill from parcadei/Continuous-Claude-v3.

How do I install Premortem in Claude Code?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill premortem -a claude-code`. Or copy the skill folder (.claude/skills/premortem in parcadei/Continuous-Claude-v3) into .claude/skills/premortem in your project. Claude Code loads it when a task matches its description.

How do I install Premortem in Codex?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill premortem -a codex`. Or copy the skill folder (.claude/skills/premortem in parcadei/Continuous-Claude-v3) into .agents/skills/premortem in your project. Codex loads it when a task matches its description.

Can I use Premortem in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add parcadei/Continuous-Claude-v3 --skill premortem -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/premortem, .gemini/skills/premortem, .github/skills/premortem and .opencode/skills/premortem in your project.

What does Premortem need to run?

SKILL.md names no scripts, command-line tools or credentials: Premortem is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Grep, Glob, Task, AskUserQuestion, TodoWrite.

Does Premortem access the network?

SKILL.md names 3 domains. As links in the text: coda.io, hbr.org and mountaingoatsoftware.com. This is read from the text; nothing was executed.

Is Premortem safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Premortem use?

Premortem is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Premortem use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Premortem?

Skills that share tags, products or a category with Premortem: Resilience Hub Failure Mode Assessment (aws/agent-toolkit-for-aws, 2.8k stars), Conducting Cyber Risk Assessment With Nist 800 30 (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Dynamic Workflow Mode (affaan-m/ECC, 276k stars) and Agent GitHub Modes (ruvnet/ruflo, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Premortem?

parcadei (a GitHub user) maintains it in parcadei/Continuous-Claude-v3, which has 3,943 GitHub stars. The repository holds 141 skills in this directory. The repository was last updated on January 26, 2026.

Source: parcadei/Continuous-Claude-v3 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.