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.
git clone --depth 1 https://github.com/vignesh2027/AI-AGENT-SKILLSWrote 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/commands/vignesh2027/ai-agent-skills/incident)<a href="https://agentmods.dev/commands/vignesh2027/ai-agent-skills/incident"><img src="https://agentmods.dev/badge/commands/vignesh2027/ai-agent-skills/incident/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/commands/vignesh2027/ai-agent-skills/incident"><img src="https://agentmods.dev/badge/commands/vignesh2027/ai-agent-skills/incident.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 | $0.00000 | $0.00204 |
| Opus 5 | $0.00000 | $0.00102 |
| Sonnet 5 | $0.00000 | $0.00041 |
| Haiku 4.5 | $0.00000 | $0.00020 |
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 9d 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.
What it actually says
Load skills/incident-response/SKILL.md.
You are the Incident Commander. Execute the incident response process:
If actively responding (P0/P1):
- Acknowledge — confirm you're on it
- Assess severity (P0/P1/P2/P3)
- Open incident channel, brief stakeholders
- Post status update (within 15 min of detection)
- Mitigate first (rollback, disable flag, scale up) — before full diagnosis
- Diagnose root cause
- Implement fix and confirm resolution
- Update status page
If writing post-mortem (after resolution): Write the post-mortem following the structure:
- Summary (what happened, duration, impact)
- Timeline (minute by minute)
- Root cause (underlying, not just trigger)
- Contributing factors
- What went well
- Action items (specific, with owners and due dates)
Blameless: focus on systems and processes, not individuals.
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.
- 9d ago First seen · 25 lines · 0 tokens per session scan A bab801d211e9
incident is a command published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (2 stars, last pushed 11d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 204 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 2026-08-31.
Other commands, from other repositories
fix
User-triggered workflow to automatically fix open issues.
debug
Command "debug" from vignesh2027/claude-best-practice, covering /debug — systematic debugging workflow, debugging protocol, phase 1: understand the problem, phase 2: gather evidence and get relevant logs.
refactor
Command "refactor" from vignesh2027/claude-best-practice, covering /refactor — safe refactoring workflow, pre-refactor safety check, refactoring rules, change types (in safe order) and level 1: rename only.
fire-resurrect
Autonomous Resurrection Mode — reverse-engineer intent from messy code, then rebuild clean from scratch.
fire-reflect
After any failure (debug resolution, test failure, approach rotation, stalled loop), capture what was tried, why it failed, and what actually worked as a persistent reflection. Future sessions search these before investigating.
fire-verify-uat
Conversational User Acceptance Testing with automatic parallel diagnosis on failures.