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Software Lifecycle

OrganizationPopular
inkeep
software-lifecycle

How to work in a Software Lifecycle project (the `software-lifecycle` starter pack): proposals → decisions → specs → postmortems, plus guides. Read when the project has these folders, or when asked how this project is organized. Carries the doc lifecycle, status flows, and per-folder agent behaviors so that guidance does not live inside template bodies or folder descriptions. The five workflows — frame a proposal, write a spec, record a decision, write a postmortem, review a design — each ship as their own sibling skill in this pack. Complements the platform `open-knowledge` skill; does not replace it.

Overview

Publisherinkeep
Repositoryopen-knowledge
Skill namesoftware-lifecycle
Stars
4.2K
Forks
279
Bundled files
1
LicenseGPL-3.0
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by inkeep on GitHub. Read the source before you install it.

Installation

Install the Software Lifecycle 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/inkeep/open-knowledge.git /tmp/open-knowledge
mkdir -p .claude/skills
cp -r /tmp/open-knowledge/packages/server/assets/skills/packs/software-lifecycle .claude/skills/software-lifecycle
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Software Lifecycle 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 Software Lifecycle 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 Software Lifecycle 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.

Software Lifecycle pack — how to work here

This project holds the doc lifecycle for an engineering team or OSS project. The flow is proposals → decisions → specs → postmortems, with guides as the how-to bucket. This skill holds the workflow so templates and folder descriptions stay clean.

This skill is pack guidance. The platform /open-knowledge skill (read/write/preview/linking/grounding rules) still governs every markdown operation — this layers the lifecycle conventions on top.

The flow

proposals/    in-flight RFC-shape design proposals
   ↓ accepted
decisions/    frozen ADRs (the record of what was decided)
   ↓ derived
specs/        implementation specs for accepted proposals
   ↓ when things break
postmortems/  blameless incident write-ups
guides/       how-to / onboarding / runbooks (referenced throughout)

Per-folder rules + agent behaviors

proposals/ — One file per proposal (0001-feature-name.md). Status flows draft → fcp → accepted/rejected. An accepted proposal graduates to a record in decisions/. Shape: Motivation / Design / Drawbacks / Alternatives / Unresolved questions. Agent: when a proposal sits at status: draft more than 14 days, surface it for the author to advance, park, or close.

decisions/ — Architecture Decision Records (MADR / Nygard shape). Frozen once accepted. One file per decision (NNNN-title.md); status proposed/accepted/deprecated/superseded. A new decision that supersedes an older one links back via Supersedes:. Agent: on a new decision, scan existing records touching the same subsystem and surface Supersedes: candidates before commit.

specs/ — Implementation specs derived from accepted proposals. Prefer the github/spec-kit shape: one folder per spec (specs/NNN-name/) with spec.md + plan.md + tasks.md (the pack ships all three templates). References the parent proposal. Agent: when a spec moves to status: shipped, suggest a postmortem template if the owner reports an incident in the spec's subsystem.

postmortems/ — Blameless incident write-ups, one file per incident (YYYY-MM-DD-name.md): Summary / Timeline / Root cause / What went well / Action items (Google SRE shape). Agent: surface a Related: block linking prior postmortems that share subsystems.

guides/ — How-to guides, onboarding docs, and service-bound runbooks (Diátaxis how-to). Ships guide, onboarding-guide, and runbook templates. Carries last_verified so stale guides surface in periodic reviews. Agent: when a postmortem is published, scan its action items for guide-shaped follow-ups and stub a guide pre-filled with the symptom and timeline excerpt.

The five workflows

Authoring in this project is procedural, not conventional. Each workflow ships as its own skill so it loads only when the work calls for it — reach for the one that matches the task rather than writing into a folder from scratch.

WorkflowSibling skillWhen
Frame a proposal/frame-a-proposalA change needs designing and arguing before anyone builds it.
Write a spec/write-a-specAn accepted proposal needs scoping into an implementable spec.
Record a decision/record-a-decisionA decision was actually made and needs its context and consequences preserved.
Write a postmortem/write-a-postmortemAn incident happened and the team needs a blameless write-up.
Review a design/review-a-designAn existing proposal, spec, or decision needs pressure-testing for soundness.

Templates

Create docs with write({ document: { path, template: "<name>" } }). Templates carry only structure (headings + frontmatter scaffold); what each section is for is described above, not repeated in the document body.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Software Lifecycle AI skill do?

How to work in a Software Lifecycle project (the `software-lifecycle` starter pack): proposals → decisions → specs → postmortems, plus guides. Read when the project has these folders, or when asked how this project is organized. Carries the doc lifecycle, status flows, and per-folder agent behaviors so that guidance does not live inside template bodies or folder descriptions. The five workflows — frame a proposal, write a spec, record a decision, write a postmortem, review a design — each ship as their own sibling skill in this pack. Complements the platform `open-knowledge` skill; does not...

Why use Software Lifecycle on TypingMind?

Because you install it once and use it with any model. Software Lifecycle 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 Software Lifecycle in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/inkeep/open-knowledge/tree/main/packages/server/assets/skills/packs/software-lifecycle. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Software Lifecycle?

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 Software Lifecycle?

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

Is the Software Lifecycle AI skill free?

Yes. It is published on GitHub by inkeep under the GPL-3.0 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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