Agently is a Python framework for building AI applications that coordinate language models, structured data, tools, and multi-step workflows. Teams use it to create assistants, internal copilots, knowledge tools, operational workflows, and AI-backed APIs.
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 AgentEra/Agently --skill incident-response-plannergit clone --depth 1 https://github.com/AgentEra/AgentlyWrote 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/agentera/agently/incident-response-planner)<a href="https://agentmods.dev/skills/agentera/agently/incident-response-planner"><img src="https://agentmods.dev/badge/skills/agentera/agently/incident-response-planner.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00037 | $0.00282 |
| Opus 5 | $0.00018 | $0.00141 |
| Sonnet 5 | $0.00007 | $0.00056 |
| Haiku 4.5 | $0.00004 | $0.00028 |
Grade A, and why
Incident Response Planner 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
Incident Response Planner
You are an SRE incident commander. Given an incident alert, produce two things in one response: a response plan and a runbook.
Response plan
Cover all six areas, be specific and actionable, avoid generic advice:
- Severity assessment (P0/P1/P2/P3) with justification.
- Impact radius (which services, users, regions are affected).
- Immediate mitigation actions (what to do right now).
- Investigation steps (what to investigate and in what order).
- Stakeholders to notify (teams, roles, external parties).
- Expected resolution timeline (best case / worst case).
Runbook
Convert the plan into a step-by-step checklist an on-call engineer can follow at 3 AM. Each step states: the action, the owner role (e.g. on-call SRE, database team, security), the expected outcome, and a verification check. Include rollback steps for any irreversible action.
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 · 30 lines · 37 tokens per session scan A c86fdf5a9d07
Incident Response Planner is a skill published in the GitHub repository AgentEra/Agently (1,649 stars, last pushed yesterday), licensed Apache-2.0. It adds 37 tokens to every session and 282 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.
Other skills, from other repositories
sre-runbooks
Safe-by-default DevOps/SRE runbook automation for incident response, postmortems, on-call handovers, and operational troubleshooting. Implements Google SRE principles with agent-safe execution patterns including dry-run modes, human approval gates, and blast-radius limits.
python-run
Run and debug Python scripts in the project. Use when the user says "run python", "execute this script", "debug this py file", or wants to run/modify a .py file. Handles dependency checks, linting, execution, and error analysis.
analyzing-windows-prefetch-with-python
Use when parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns. Use when working with analyzing windows prefetch with python.
sre_triage
SRE first-response triage for distributed training incidents. Automates the manual checks from PyTorch/NCCL debugging runbooks.
python
Python package management.
design-incident-response
Use when creating or improving an incident response process for a production system or operational team.