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 skills add caiaffa/claude-code-ultimate-engineering-system --skill premortem-facilitatorgit clone --depth 1 https://github.com/caiaffa/claude-code-ultimate-engineering-systemWrote 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/caiaffa/claude-code-ultimate-engineering-system/premortem-facilitator)<a href="https://agentmods.dev/skills/caiaffa/claude-code-ultimate-engineering-system/premortem-facilitator"><img src="https://agentmods.dev/badge/skills/caiaffa/claude-code-ultimate-engineering-system/premortem-facilitator.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 5.1 | $0.00034 | $0.00595 |
| Opus 5 | $0.00017 | $0.00298 |
| Sonnet 5 | $0.00007 | $0.00119 |
| Haiku 4.5 | $0.00003 | $0.00060 |
Grade A, and why
premortem-facilitator 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 6d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mission
Find the most likely reasons an initiative will fail before production experiences them.
When to use
- Planning a risky feature.
- Preparing a migration or rollout.
- Evaluating distributed or operationally sensitive changes.
- Any launch where "we'll see how it goes" is the implicit plan.
Handoff
- Receives from: release-commander (pre-launch) or principal-engineer (design phase).
- Hands off to: release-commander (rollout plan adjustments), backend-platform-engineer (design changes), otel-observability-architect (monitoring gaps).
The premortem method
Step 1: "It's 30 days after launch. This change caused a serious production problem."
Step 2: Work backwards. What happened? Why?
Step 3: For each scenario, identify the safeguard that should exist but doesn't.
Step 4: Decide which safeguards to add BEFORE launch.
Failure scenario generation
Generate scenarios across these categories:
| Category | Example scenario |
|---|---|
| Dependency | External API becomes 10x slower (not down) |
| Data | Migration corrupts 0.1% of records silently |
| Scale | Feature gets 50x expected usage on day 1 |
| Compatibility | Old mobile app sends requests new API doesn't handle |
| Rollback | Need to revert but database schema already migrated |
| Operational | Alert fatigue causes team to ignore first warning signs |
| Business | Feature works technically but metric doesn't improve |
For each scenario, answer
- What failed? (specific, not generic)
- What signal should have caught it? (metric, alert, test)
- Why wasn't it caught before release? (missing test? missing review?)
- What safeguard was missing? (circuit breaker? validation? canary?)
- What design change would make this much less likely?
Red flags — premortem is too shallow if
- All scenarios are "dependency goes down" variations.
- No scenarios about data correctness.
- No scenarios about rollback/revert failures.
- No scenarios about business metric not moving.
- Scenarios are generic, not tied to specific design choices.
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.
- 6d ago First seen · 60 lines · 34 tokens per session scan A 05c9acb8a890
premortem-facilitator is a skill published in the GitHub repository caiaffa/claude-code-ultimate-engineering-system (17 stars, last pushed 2mo ago), licensed MIT. It adds 34 tokens to every session and 595 once invoked, about $0.0002 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 2026-08-30.
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