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 trilwu/secskills --skill responding-to-incidentsgit clone --depth 1 https://github.com/trilwu/secskillsWrote 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/trilwu/secskills/responding-to-incidents)<a href="https://agentmods.dev/skills/trilwu/secskills/responding-to-incidents"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/responding-to-incidents/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/trilwu/secskills/responding-to-incidents"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/responding-to-incidents.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 | $0.00074 | $0.03872 |
| Opus 5 | $0.00037 | $0.01936 |
| Sonnet 5 | $0.00015 | $0.00774 |
| Haiku 4.5 | $0.00007 | $0.00387 |
Grade D, and why
responding-to-incidents scanned grade D with 2 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 12d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo ./avml mem.lime # or LiME Reaches for credential fileshighPrivilege escalation
SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.
| Lateral movement? | 4624 type 3/10, 4648, 4672, RDP logs, SMB shares | `auth.log`, `wtmp`, `.ssh/authorized_keys`, known_hosts | Same as Linux plus ARD logs | Copies of this mod
1 near-identical copy found in the catalogue:
- responding-to-incidents — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 331 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Responding to Incidents
Two questions drive every incident: how far did they get, and are they still here. Everything else — the malware, the CVE, the root cause — is supporting detail. Answer those two in order and the response follows.
When to Use
- Suspected or confirmed compromise of a host, account, or cloud tenant
- Forensic analysis of a disk image, memory capture, or log set
- Reconstructing what an attacker did and when
- Scoping blast radius and deciding containment
- Writing a postmortem or a regulator/customer-facing incident narrative
When NOT to Use
- Deep analysis of a recovered sample — use
analyzing-malware - Proactive searching with no known incident — use
hunting-threats - Deciding whether an alert is even an incident yet — use
triaging-security-alerts; response begins once triage confirms a true positive - Building the detections that would have caught it — use
engineering-detections - The incident is in AWS (exposed keys, CloudTrail/GuardDuty) — use
investigating-aws-incidents; in Microsoft 365 / Entra — useinvestigating-m365-entra - You have a packet capture to work through — use
analyzing-network-traffic - Service outages with no security dimension — this is a security IR skill
Route to a Depth Skill
Specific evidence types have their own procedure skill. This skill sets scope and order; reach for these when a single artifact type becomes the focus.
| Artifact / focus | Skill |
|---|---|
| A RAM capture to work through with Volatility (injected code, in-memory creds, dead-process connections) | analyzing-memory-images |
| A Microsoft 365 / Entra ID compromise: no disk or memory, only cloud logs (UAL, sign-ins, OAuth grants) | investigating-m365-entra |
| Finding how an attacker persisted on a Linux host — the systematic sweep across every init path | analyzing-linux-persistence |
The Order That Matters
Preparation → Detection & Analysis → Containment → Eradication → Recovery → Lessons Learned
↑__________________|
(re-scope after every new finding)
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.
- 12d ago First seen · 331 lines · 74 tokens per session scan D e8f00ef1142b
responding-to-incidents is a skill published in the GitHub repository trilwu/secskills (138 stars, last pushed 7d ago), licensed MIT. It adds 74 tokens to every session and 3,872 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it D with 2 findings (asks for root, reaches for credential files). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
comet-memory
A review step for deciding whether information should become durable personal memory. It can keep, update, forget, or skip memory candidates based on bounded evidence.
recall-memory
Recall relevant long-term memories on demand. Given a topic or question, judges relevance from pre-loaded metadata, loads only relevant files, and returns a concise summary to the main agent.
agent-expert-creation
Create specialized agent experts with pre-loaded domain knowledge using the Act-Learn-Reuse pattern. Use when building domain-specific agents that maintain mental models via expertise files and self-improve prompts.
relevance-coarse-filter
Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitoronly, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.
self-improve
Extract lessons from the current session, or sweep the project's past sessions when asked, and route them to the appropriate knowledge layer (project AGENTS.md, auto memory, existing skills, or new skills). Use when the user asks to "self-improve", "distill this session", "distill past sessions", "sweep past…