adr-writer
Use when capturing an architecture decision so it survives turnover — produces an ADR-NNNN.md from context, options considered,…
Use when opening a PR — produces a clean PR description (what / why / how to verify / risks) from a branch diff against base.
$ npx -y skills add oxbshw/LLM-Agents-Ecosystem-Handbook --skill pr-summarizer --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/pr-summarizerContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when opening a PR — produces a clean PR description (what / why / how to verify / risks) from a branch diff against base.
name: pr-summarizer description: Use when opening a PR — produces a clean PR description (what / why / how to verify / risks) from a branch diff against base. version: 0.1.0 status: experimental risk: low tags: [engineering, read-only, writes-files]
| Name | Type | Required | Notes | |---|---|---|---| | `base` | string | no | default `origin/main` | | `branch` | string | no | default current `HEAD` |
A markdown PR body with sections: **Summary**, **Why**, **How tested**, **Risks**, **Linked**.
1. `git diff <base>...HEAD --stat` and `git log <base>..HEAD --oneline` to see scope 2. `git diff <base>...HEAD` for content 3. Group changes by area (one bullet per logical concern, not per file) 4. Pull "why" from commit messages + linked tickets if mentioned 5. Identify test artifacts touched; surface as **How tested** (or flag if none) 6. Risks: any High-risk paths touched? auth, billing, migrations? mention. 7. Linked: parse `Fixes #` / `Refs #` from commits
A practical operating manual for building, evaluating, securing, and shipping modern LLM agent systems. Modern agents are not "a prompt + a tool."
Use when capturing an architecture decision so it survives turnover — produces an ADR-NNNN.md from context, options considered,…
Use when reviewing a proposed REST or GraphQL API change before merge — checks contract clarity, backwards compatibility, errors,…
Use when first encountering a new dataset — produces a structured profile (schema, missingness, distributions, outliers, gotchas)…
Use after an incident is resolved — drafts a blameless postmortem from timeline notes, alerts, and chat threads.
Use when planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals.
Use after a session to promote useful episodic notes from logs/episodic/ into distilled, dated entries in MEMORY.md and…