Borrowing it
Nothing to install: this file belongs to AsiaOstrich/EngramGraph. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AsiaOstrich/EngramGraph/main/.agents/skills/incident-response-assistant/SKILL.mdgit clone --depth 1 https://github.com/AsiaOstrich/EngramGraphWrote 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/asiaostrich/engramgraph/incident-response-assistant)<a href="https://agentmods.dev/skills/asiaostrich/engramgraph/incident-response-assistant"><img src="https://agentmods.dev/badge/skills/asiaostrich/engramgraph/incident-response-assistant/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/asiaostrich/engramgraph/incident-response-assistant"><img src="https://agentmods.dev/badge/skills/asiaostrich/engramgraph/incident-response-assistant.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00078 | $0.01981 |
| Opus 5.5 | $0.00031 | $0.00792 |
| Sonnet 5.5 | $0.00016 | $0.00396 |
| Haiku 4.5 | $0.00008 | $0.00198 |
Grade A, and why
incident 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 14d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incident Response Assistant | 事故回應助手
Language: English | 繁體中文
Guide structured incident response from detection through post-mortem.
引導結構化的事故回應流程，從偵測到事後檢討。
Severity Classification | 嚴重程度分類
| Level | Name | Criteria | 標準 | Response Time |
|---|---|---|---|---|
| SEV-1 | Critical 重大 | Full service outage, data loss | 全面服務中斷、資料遺失 | Immediate (< 15 min) |
| SEV-2 | High 高 | Major feature degraded, partial outage | 主要功能降級、部分中斷 | < 30 min |
| SEV-3 | Medium 中 | Minor feature affected, workaround exists | 次要功能受影響、有替代方案 | < 4 hours |
| SEV-4 | Low 低 | Cosmetic issue, minimal user impact | 外觀問題、最小用戶影響 | Next business day |
Response Workflow | 回應工作流程
DETECT ──► TRIAGE ──► MITIGATE ──► RESOLVE ──► POST-MORTEM ──► IMPROVE
偵測 分級 緩解 解決 事後檢討 持續改善
1. Detect — Identify the Incident | 偵測事故
- Monitor alerts, user reports, error spikes
- 監控告警、使用者回報、錯誤量飆升
2. Triage — Classify Severity | 分級嚴重程度
- Assign SEV level, identify Incident Commander (IC)
- 指定嚴重等級、識別事故指揮官（IC）
3. Mitigate — Reduce Impact | 緩解影響
- Apply temporary fix: rollback, feature flag, traffic shift
- 套用暫時修復：回滾、功能開關、流量切換
4. Resolve — Permanent Fix | 永久修復
- Root cause analysis, implement proper fix, deploy
- 根因分析、實作正確修復、部署
5. Post-Mortem — Document & Analyze | 事後檢討
- Document timeline, impact, root cause, action items
- 記錄時間軸、影響範圍、根因、行動項目
6. Improve — Track & Prevent | 持續改善
- Track action item completion, analyze incident trends, prevent recurrence
- 追蹤行動項目完成度、分析事故趨勢、防止再發
Post-Mortem Template | 事後檢討模板
## Post-Mortem: [Incident Title]
**Date**: YYYY-MM-DD | **Severity**: SEV-N | **Duration**: Xh Ym
### Timeline
| Time | Event |
|------|-------|
| HH:MM | Alert triggered / 告警觸發 |
| HH:MM | IC assigned / 指派事故指揮官 |
| HH:MM | Mitigation applied / 套用緩解措施 |
| HH:MM | Resolved / 解決 |
### Impact
- Users affected / 受影響用戶數: N
- Revenue impact / 營收影響: $N
- SLA breach / SLA 違反: Yes/No
### Root Cause
[Description of the underlying cause / 根本原因描述]
### Action Items
| Action | Owner | Due Date | Priority | Status |
|--------|-------|----------|----------|--------|
| [Fix] | @name | YYYY-MM-DD | P0 | Open |
### Incident Metrics
- MTTR (Mean Time To Recovery): Xh Ym
- Detection Time: Xm (alert to IC assigned)
- Recurrence: First / Repeat (link to previous)
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.
- 14d ago First seen · 182 lines · 78 tokens per session scan A 239802a2acfa
incident is a skill published in the GitHub repository AsiaOstrich/EngramGraph (3 stars, last pushed today), licensed MIT. It adds 78 tokens to every session and 1,981 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-10-16.
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