adr-writer
Use when capturing an architecture decision so it survives turnover — produces an ADR-NNNN.md from context, options considered,…
Use when planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals.
$ npx -y skills add oxbshw/LLM-Agents-Ecosystem-Handbook --skill sprint-planner --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/sprint-plannerContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals.
name: sprint-planner description: Use when planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals. version: 0.1.0 status: experimental risk: low tags: [product, writes-files]
| Name | Type | Required | Notes | |---|---|---|---| | `tickets` | list / path | yes | candidate work items with priority + estimates | | `capacity` | object | yes | per-engineer days available | | `previous_carry` | list | no | items rolling over from last sprint | | `commitments` | list | no | OKR / stakeholder commitments that must land |
`sprint-plan.md` with: Sprint goal, Committed, Stretch, Non-goals, Risks.
1. Compute total capacity (days × team) 2. Sort candidates: committed → carry → stretch 3. Pack to ~85% of capacity (leave room for support / interrupts) 4. Identify dependencies between items; surface blockers 5. Surface **explicit non-goals** — what we are *not* doing this sprint and why 6. Risks: anything that would cause spillover
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 opening a PR — produces a clean PR description (what / why / how to verify / risks) from a branch diff against base.
Use after a session to promote useful episodic notes from logs/episodic/ into distilled, dated entries in MEMORY.md and…