Agile Development logo

Agile Development

Organization
serac-labs
agile-development

ServiceNow Agile Development 2.0 — the rm_team → rm_sprint → rm_story → rm_epic model, the state and point values ServiceNow actually accepts, how a story is attached to a sprint, and the reporting tools (board, burndown, velocity, capacity, standup, retrospective).

Overview

Publisherserac-labs
Repositoryserac
Skill nameagile-development
Stars
78
Forks
26
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by serac-labs on GitHub. Read the source before you install it.

Installation

Install the Agile Development AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/serac-labs/serac.git /tmp/serac
mkdir -p .claude/skills
cp -r /tmp/serac/packages/skills/agile-development .claude/skills/agile-development
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agile Development in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agile Development on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agile Development is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Agile Development 2.0

Sixteen tools on rm_* and pm_*. This is the plugin-provided Agile module (com.snc.sdlc.agile.2.0), not a generic task tracker — the tables are its own and the state values are numbers behind friendly labels.

Is the plugin even on?

Every table here comes from the plugin. If it is not activated, calls fail with "Invalid table", and the tools say so with the plugin id rather than passing the raw error through. Cheapest check — a read that needs no ids:

javascript
await snow_agile_sprint_query({ limit: 1 })   // touches rm_sprint

An instance without the plugin has none of this and no amount of retrying helps.

The model

rm_team          a scrum team, with a default velocity
  └── rm_team_member    one person on it, with a role
rm_sprint        a time box, optionally assigned to a team
rm_epic          a body of work spanning sprints
  └── rm_story        the unit of work. Points, state, assignee.
       └── rm_scrum_task   optional breakdown under a story
rm_release       groups sprints and epics for a ship date

Separately, pm_project / pm_project_task is PPM, a different product: waterfall-ish projects with a manager and dates. snow_create_project and snow_create_project_task write there. Do not mix them with the rm_* tables — a story is not a project task and nothing joins them.

Order of operations

Team → sprint → story. Every link is a reference lookup that fails if the target is not there yet.

javascript
// 1. team
const team = await snow_agile_team_manage({ action: "create", name: "Platform", velocity: 30 })
await snow_agile_team_manage({ action: "add_member", sys_id: team.sys_id,
                               member: "sam.patel", role: "scrum_master" })

// 2. sprint — the team is resolved by name or sys_id
await snow_agile_sprint_manage({ action: "create", name: "Sprint 24", team: "Platform",
                                 start_date: "2026-09-01", end_date: "2026-09-14", story_points: 30 })

// 3. stories, attached at creation or later
await snow_agile_story_manage({ action: "create", title: "Widen the assignment lookup",
                                story_points: 5, sprint: "Sprint 24", state: "Ready" })

Two things the schema does not tell you:

  • A sprint's name is written to short_description. rm_sprint has no name column; the tool maps it. Query on short_description, not name.
  • team is stored as assignment_group. The tool resolves the team through rm_team and writes the sys_id into the sprint's assignment_group. A sprint with no team is legal — omit the parameter.

The values ServiceNow accepts

Story state is a friendly label in the tool and a number on the record:

Labelrm_story.state
Draft-6
Ready-5
Work In Progress2
Testing-2
Closed Complete3
Closed Incomplete4

Pass the label — a raw number is not translated and lands as-is. Anything not in that table is dropped silently rather than rejected, so a typo means the state simply does not change.

Sprint state is not a parameter at all. action: "start" writes 2, action: "close" writes 3. There is no "reopen".

Story points are a Fibonacci-ish scale: 1, 2, 3, 5, 8, 13, 21. The field is a plain number and will take 7; every report that buckets by points then has an off-scale bucket. Estimate on the scale.

Priority is 1–4, Critical through Low, on both stories and epics — the ITSM 1–5 convention does not apply here.

Attaching a story to a sprint

sprint on snow_agile_story_manage accepts a sprint sys_id or number, and the tool resolves it through rm_sprint before writing. Same for epic. That means:

  • A sprint that does not exist yet is a failed lookup, not an auto-create.
  • Moving a story between sprints is an update with the new sprint, not a delete and recreate — the points history stays with the story, which is what burndown reads.

For more than one story, use the grooming tool instead of a loop:

javascript
await snow_agile_backlog_groom({ action: "assign_sprint", sprint: "Sprint 24",
                                 stories: [{ sys_id: storyA }, { sys_id: storyB }, { sys_id: storyC }] })
await snow_agile_backlog_groom({ action: "estimate",
                                 stories: [{ sys_id: storyA, story_points: 8 }] })

stories is always an array of objects with sys_id required, even for the actions that carry their target in a separate parameter — a bare array of sys_ids is rejected. priority, story_points and order go on the item; sprint and epic go on the call.

Its four actions are reprioritize, estimate, assign_sprint and move_epic — the four things grooming actually is. Each story is patched independently and the response carries a per-story updated / skipped / error status, so a partial failure is visible rather than fatal.

Reading a board

javascript
// stories grouped by state column; falls back to the team's active sprint
await snow_agile_sprint_board({ team: "Platform", include_tasks: true })

// the backlog: unassigned or unestimated work is the useful filter
await snow_agile_backlog_query({ unassigned_only: true, has_points: false, limit: 50 })

snow_agile_backlog_query paginates with limit/offset and defaults to 25. A backlog is usually longer than that — a result of exactly 25 means truncated.

Reporting

Five tools that read and never write. They differ in what they need:

ToolNeedsAnswers
snow_agile_sprint_burndowna sprintideal vs actual points per day, scope changes
snow_agile_velocity_reporta teamcommitted vs completed per sprint, last 6
snow_agile_capacity_plana teamavailability vs commitment, recommended capacity
snow_agile_standup_reporta teamwho did what since yesterday, and blockers
snow_agile_retrospectivea sprint or teamplanned vs completed, carry-over, defect rate

capacity_plan averages velocity over velocity_sprints (3 by default), so on a team with fewer than three closed sprints its recommendation is an average of very little. retrospective defaults to the last closed sprint when no sprint is given — running it mid-sprint reports on the previous one, which is usually what you want and occasionally a surprise.

Blockers come from the story: blocked and blocked_reason on rm_story. If standup reports no blockers on a visibly stuck team, nobody is setting the flag — the report is accurate and the data is not.

Releases

javascript
await snow_agile_release_manage({ action: "readiness", sys_id: releaseSysId })

readiness is the useful one: it reports story completion across the sprints and epics linked to the release. create/update take a state from Draft, Planning, In Progress, Released, Cancelled.

Related

  • update-set-workflow — none of this is configuration. Stories and sprints are data; they are not captured in an update set and do not move between instances that way.
  • request-management — the other place work items live, and a different set of tables entirely.

Frequently asked questions

What does the Agile Development AI skill do?

ServiceNow Agile Development 2.0 — the rm_team → rm_sprint → rm_story → rm_epic model, the state and point values ServiceNow actually accepts, how a story is attached to a sprint, and the reporting tools (board, burndown, velocity, capacity, standup, retrospective).

Why use Agile Development on TypingMind?

Because you install it once and use it with any model. Agile Development is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agile Development in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/serac-labs/serac/tree/main/packages/skills/agile-development. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agile Development?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agile Development?

As many as you like. As long as a model supports skills, you can use Agile Development with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agile Development AI skill free?

Yes. It is published on GitHub by serac-labs under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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