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 agentmods add commands/kid-sid/claude-spellbook/postmortemgit clone --depth 1 https://github.com/kid-sid/claude-spellbookWhat 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 | $0.00000 | $0.00971 |
| Opus 5 | $0.00000 | $0.00485 |
| Sonnet 5 | $0.00000 | $0.00194 |
| Haiku 4.5 | $0.00000 | $0.00097 |
Grade A, and why
postmortem 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 3d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate a postmortem document from an incident description.
Instructions
-
Ask the user (if not already specified):
- Incident title: brief description of what happened
- Severity: SEV1 (critical) / SEV2 (major) / SEV3 (minor) / SEV4 (low)
- Duration: when did it start and end?
- Impact: what was affected? (users, services, revenue)
-
Generate the postmortem using this blameless template:
# Postmortem: {Incident Title}
**Date:** {YYYY-MM-DD}
**Severity:** {SEV1/SEV2/SEV3/SEV4}
**Duration:** {start time} — {end time} ({total duration})
**Authors:** {names}
**Status:** Draft | In Review | Final
## Executive Summary
{2-3 sentence summary: what happened, what was the impact, and is it fully resolved?}
## Impact
| Metric | Value |
|--------|-------|
| Users affected | {number or percentage} |
| Duration of impact | {duration} |
| Revenue impact | {estimated $} |
| SLA impact | {e.g., dropped below 99.9%} |
| Support tickets | {count} |
## Timeline (all times in UTC)
| Time | Event |
|------|-------|
| {HH:MM} | {First signal: alert fired / user report / monitoring} |
| {HH:MM} | {Detection: who noticed and how} |
| {HH:MM} | {Escalation: who was paged} |
| {HH:MM} | {Investigation: what was checked first} |
| {HH:MM} | {Mitigation: what stopped the bleeding} |
| {HH:MM} | {Resolution: root cause fixed} |
| {HH:MM} | {All-clear: confirmed recovery} |
## Root Cause
{Detailed technical explanation of what went wrong and why. Be specific — name the exact component, config, or code path.}
## Detection
- **How was it detected?** {alert / user report / manual check}
- **Time to detect (TTD):** {duration from start to detection}
- **Could we have detected it sooner?** {yes/no and how}
## Mitigation & Resolution
### Immediate mitigation
{What was done to stop the impact? (rollback, feature flag, scaling, etc.)}
### Root cause fix
{What was done to permanently fix the underlying issue?}
## Contributing Factors
{What conditions allowed this to happen? Think systemic, not individual.}
- {factor 1: e.g., missing integration test for this code path}
- {factor 2: e.g., no alerting on this specific error class}
- {factor 3: e.g., deploy happened outside normal hours without extra review}
## Lessons Learned
### What went well
- {thing that worked: e.g., alerting fired within 2 minutes}
- {thing that worked: e.g., runbook was accurate and up-to-date}
### What went poorly
- {thing that failed: e.g., took 30 min to identify the failing service}
- {thing that failed: e.g., no rollback automation}
### Where we got lucky
- {thing that could have been worse: e.g., happened during low-traffic hours}
## Action Items
| Priority | Action | Owner | Due Date | Ticket |
|----------|--------|-------|----------|--------|
| P0 | {critical fix} | {name} | {date} | {link} |
| P1 | {important improvement} | {name} | {date} | {link} |
| P2 | {nice-to-have improvement} | {name} | {date} | {link} |
## Appendix
### Related Links
- {monitoring dashboard}
- {relevant PR or commit}
- {Slack thread}
- {alert configuration}
### Raw Data
{Any relevant logs, graphs, or metrics snapshots}
-
Pre-fill the timeline by:
- Checking git log for recent deploys around the incident time
- Looking for relevant error patterns in the codebase
- Suggesting contributing factors based on code review
-
Save the file to
docs/postmortems/{YYYY-MM-DD}-{kebab-case-title}.md(create directory if needed).
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.
- 3d ago First seen · 118 lines · 0 tokens per session scan A 6fd547b1284a
postmortem is a command published in the GitHub repository kid-sid/claude-spellbook (187 stars, last pushed 27d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 971 tokens. 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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