Agent skill

Sprint Planner

by oxbshw in oxbshw/LLM-Agents-Ecosystem-Handbook

A skill your agent uses when planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals.

MITAuto-check passedAI & LLM Engineering

Install Sprint Planner

skills CLI
$ npx skills add oxbshw/LLM-Agents-Ecosystem-Handbook --skill sprint-planner -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install oxbshw/LLM-Agents-Ecosystem-Handbook sprint-planner --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/oxbshw/LLM-Agents-Ecosystem-Handbook.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/catalog/sprint-planner .claude/skills/sprint-planner && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
sprint-planner
GitHub stars
552
Token cost
~429 tokens
SKILL.md length
183 words
Files
2 (incl. references)
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals.

  • Works in 6 steps: Compute total capacity (days × team) → Sort candidates: committed → carry →… → Pack to ~85% of capacity (leave room for… → …
  • Planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals
  • SKILL.md covers When to use, When NOT to use, Inputs and Outputs, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Sprint Planner is an agent skill from oxbshw/LLM-Agents-Ecosystem-Handbook. Use when planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals.

Its SKILL.md is about 430 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/sprint-plan-template.md`).

It sits in AI & LLM Engineering. The repository describes itself as: One-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials, ecosystem guides, and evaluation tools. The licence is MIT.

When your agent uses it

  • Planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals

Example prompts

  • “/sprint-planner”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  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

What it can do on your machine

Read from SKILL.md and the folder at commit 7f8ee3c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Sprint Planner loads about 429 tokens when it runs, and up to ~580 if it reads all its reference files. Until then it costs about 33 tokens; SKILL.md has 183 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~429
With references · SKILL.md plus every file in references/, read only if the agent opens them
~580

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from oxbshw/LLM-Agents-Ecosystem-Handbook at commit 7f8ee3c, republished under its MIT licence (© oxbshw). 183 words, ~429 tokens.

Download SKILL.mdSave it as .claude/skills/sprint-planner/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
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

Sprint Planner

When to use

  • Start of a sprint cycle
  • After a re-prioritization mid-cycle
  • When intake outpaces capacity and you need a defendable plan

When NOT to use

  • Daily standup (different cadence)
  • Annual planning (different scope; use roadmap-synthesizer)

Inputs

NameTypeRequiredNotes
ticketslist / pathyescandidate work items with priority + estimates
capacityobjectyesper-engineer days available
previous_carrylistnoitems rolling over from last sprint
commitmentslistnoOKR / stakeholder commitments that must land

Outputs

sprint-plan.md with: Sprint goal, Committed, Stretch, Non-goals, Risks.

Workflow

  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

References

Success criteria

  • Committed work ≤ 85% of capacity
  • Every committed item has an owner and an estimate
  • Non-goals section is non-empty
  • Risks include at least one "what could derail us"

Failure modes

  • Capacity unknown → ask, don't guess
  • Tickets without estimates → flag and exclude from "committed"

© oxbshw, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/catalog/sprint-planner of oxbshw/LLM-Agents-Ecosystem-Handbook.

  • SKILL.md
  • references/sprint-plan-template.md

Open the folder on GitHubat commit 7f8ee3c

Compare with similar skills

Sprint Planner next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Sprint Planner compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Sprint Planner this skilloxbshw/LLM-Agents-Ecosystem-Handbook552—~429Automated safety check: PassMIT
Controlww-w-ai/bkit-claude-code601—~1.6kAutomated safety check: NotesApache-2.0
Agent Specmohitagw15856/pm-claude-skills1.4k—~1.1kAutomated safety check: PassMIT
Project Status Reportmohitagw15856/pm-claude-skills1.4k—~954Automated safety check: PassMIT
Factory Learntikalk/adlc-team-skills141—~1.5kAutomated safety check: PassMIT
Agent BuildershareAI-lab/learn-claude-code78k5 repos~1.2kAutomated safety check: PassMIT

Similar skills

  • Control

    ww-w-ai/bkit-claude-code

    Control bkit automation level (L0-L4), view trust score, and manage guardrails.

    601 GitHub stars~1.6k tokensUpdated 12 days ago
    AI & LLM EngineeringAuto-check: notes
  • Agent Spec

    mohitagw15856/pm-claude-skills

    Specify an autonomous or tool-using AI agent before building it.

    1.4k GitHub stars~1.1k tokensUpdated yesterday
    AI & LLM EngineeringAuto-check passed
  • Project Status Report

    mohitagw15856/pm-claude-skills

    Write a structured project status report for any project. An agent skill from mohitagw15856/pm-claude-skills.

    1.4k GitHub stars~954 tokensUpdated yesterday
    AI & LLM EngineeringAuto-check passed
  • Factory Learn

    tikalk/adlc-team-skills

    A skill your agent uses when coordinating continuous improvement loops (team-levelup + change + evals feedback + cleanup) targeting team-ai-directives — includes build-to-delete pruning and…

    141 GitHub stars~1.5k tokensUpdated yesterday
    AI & LLM EngineeringAuto-check passed
  • Agent Builder

    shareAI-lab/learn-claude-code

    Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.

    78k GitHub starsUsed in 5 repos~1.2k tokens
    AI & LLM EngineeringAuto-check passed
  • Add Uint Support

    pytorch/pytorch

    Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.

    104k GitHub starsUsed in 2 repos~2.3k tokens
    AI & LLM EngineeringAuto-check passed

More from oxbshw/LLM-Agents-Ecosystem-Handbook

  • API Design Reviewer

    oxbshw/LLM-Agents-Ecosystem-Handbook

    A skill your agent uses when reviewing a proposed REST or GraphQL API change before merge — checks contract clarity, backwards compatibility, errors, pagination, auth, and naming.

    552 GitHub stars~553 tokensUpdated 3 mo ago
    Auto-check passed
  • Dataset Profiler

    oxbshw/LLM-Agents-Ecosystem-Handbook

    A skill your agent uses when first encountering a new dataset — produces a structured profile (schema, missingness, distributions, outliers, gotchas) before any analysis.

    552 GitHub stars~492 tokensUpdated 3 mo ago
    Auto-check passed
  • PR Summarizer

    oxbshw/LLM-Agents-Ecosystem-Handbook

    A skill your agent uses when opening a PR — produces a clean PR description (what / why / how to verify / risks) from a branch diff against base.

    552 GitHub stars~441 tokensUpdated 3 mo ago
    Auto-check: notes
  • Adr Writer

    oxbshw/LLM-Agents-Ecosystem-Handbook

    A skill your agent uses when capturing an architecture decision so it survives turnover — produces an ADR-NNNN.md from context, options considered, and the chosen path.

    552 GitHub stars~476 tokensUpdated 3 mo ago
    Auto-check passed
  • Incident Postmortem

    oxbshw/LLM-Agents-Ecosystem-Handbook

    Use after an incident is resolved — drafts a blameless postmortem from timeline notes, alerts, and chat threads.

    552 GitHub stars~461 tokensUpdated 3 mo ago
    Auto-check passed

Questions about Sprint Planner

What does Sprint Planner do?

A skill your agent uses when planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals. Sprint Planner is an agent skill from oxbshw/LLM-Agents-Ecosystem-Handbook. Use when planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals.

When should I use Sprint Planner?

Sprint Planner fits situations like: planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals.

How do I install Sprint Planner in Claude Code?

Run `npx skills add oxbshw/LLM-Agents-Ecosystem-Handbook --skill sprint-planner -a claude-code`. Or copy the skill folder (skills/catalog/sprint-planner in oxbshw/LLM-Agents-Ecosystem-Handbook) into .claude/skills/sprint-planner in your project. Claude Code loads it when a task matches its description.

How do I install Sprint Planner in Codex?

Run `npx skills add oxbshw/LLM-Agents-Ecosystem-Handbook --skill sprint-planner -a codex`. Or copy the skill folder (skills/catalog/sprint-planner in oxbshw/LLM-Agents-Ecosystem-Handbook) into .agents/skills/sprint-planner in your project. Codex loads it when a task matches its description.

Can I use Sprint Planner in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add oxbshw/LLM-Agents-Ecosystem-Handbook --skill sprint-planner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sprint-planner, .gemini/skills/sprint-planner, .github/skills/sprint-planner and .opencode/skills/sprint-planner in your project.

What does Sprint Planner need to run?

SKILL.md names no scripts, command-line tools or credentials: Sprint Planner is instructions for the agent only.

Does Sprint Planner access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Sprint Planner safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Sprint Planner use?

Sprint Planner is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Sprint Planner use?

About 429 tokens (SKILL.md is roughly 1.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 151 tokens, read only when the agent opens those files.

What are the alternatives to Sprint Planner?

Skills that share tags, products or a category with Sprint Planner: Control (ww-w-ai/bkit-claude-code, 601 stars), Agent Spec (mohitagw15856/pm-claude-skills, 1.4k stars), Project Status Report (mohitagw15856/pm-claude-skills, 1.4k stars) and Factory Learn (tikalk/adlc-team-skills, 141 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sprint Planner?

oxbshw (a GitHub user) maintains it in oxbshw/LLM-Agents-Ecosystem-Handbook, which has 552 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on June 30, 2026.

Source: oxbshw/LLM-Agents-Ecosystem-Handbook on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.