create-milestone
Skill4 starsChanged 8 days agoMITView on GitHub
Primary planning branch after /shape for major-tranche work that requires multiple independent PRDs. Use to create a GitHub milestone plus sequenced feature issues that will later mature from roadmap bet to research-ready to prd. Not for single-feature shaping, single-PRD big-batch work (which gets a container milestone from /write-a-prd), decomposition of a finished PRD, or implementation-ready work.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- agentic-community/mcp-gateway-registrytheir own version, first seen Nov 6, 2026
- Jamie-BitFlight/claude_skillstheir own version, first seen Sep 23, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- agentic-community/mcp-gateway-registry★ 1ksame name
- Jamie-BitFlight/claude_skills★ 67same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository36 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- api-design-reviewSkill
- closeoutSkill
- compoundSkill
- correct-courseSkill
- design-an-interfaceSkill
- excavateSkill
- executeSkill
- fix-findingsSkill
- git-guardrails-claude-codeSkill
- hackathonSkill
- handoffSkill
- helpSkill
- improve-codebase-architectureSkill
- improve-pipelineSkill
- init-pipelineSkill
- lfgSkill
- mermaidSkill
- prd-to-issuesSkill
- pre-mergeSkill
- prototypeSkill
- qaSkill
- re-pitchSkill
- request-refactor-planSkill
- researchSkill
- respond-to-reviewSkill
- setup-pre-commitSkill
- setup-ralph-loopSkill
- shapeSkill
- tddSkill
- triage-issueSkill
- ts-auditSkill
- ubiquitous-languageSkill
- visual-recapSkill
- walk-commitsSkill
- write-a-prdSkill
The file
Show its rules and text
We split this skill into the rules it gives an agent
18 rules. About 0 are common, 10 are distinctive, and 8 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈10
Distinctive · estimate
This author's own choices. Worth a look.
8
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 33
Do not use it when the work already fits inside one PRD, when a feature is already research-ready, or when the next need is decomposition …
- Distinctive: line 47
Do not create a duplicate PRD issue by default.
- Distinctive: line 53
Before creating anything, confirm that the shaped work is too large for a single PRD.
- Distinctive: line 55
Ask these questions one at a time — ask one, wait for the answer, then proceed to the next:
- Very specific: line 61
If the answers suggest the work already fits one bounded pitch, do not continue.
- Distinctive: line 65
Create one GitHub milestone representing the tranche or v1 scope.
- Distinctive: line 74
Do not turn the milestone itself into a giant spec.
- Distinctive: line 78
Create a small set of feature issues attached to the milestone.
- Distinctive: line 80
Keep the set deliberately limited.
- Very specific: line 93
If labels are available, add a bet label alongside the written maturity field.
- Distinctive: line 103
Avoid sequencing by convenience alone.
- Very specific: line 107
When a feature is selected to enter the delivery pipeline, expand that same issue from roadmap bet to research-ready before invoking …
- Very specific: line 120
If labels are available, move the issue label from bet to research-ready.
- Very specific: line 122
Do not send a raw roadmap bet into /research.
- Very specific: line 137
If /research shows the milestone-level assumptions were materially wrong, backtrack to /create-milestone or all the way to /shape rather …
- Distinctive: line 141
When the milestone's feature issues that define the tranche are complete, close the GitHub milestone.
- Very specific: line 143
If the tranche taught something reusable beyond a single feature, /compound may capture tranche-level lessons after closeout.
- Distinctive: line 147
Use this structure for each new milestone feature issue:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Create Milestone
This skill bridges the gap between tranche-level shaping and feature-level delivery. Use it when /shape clarified work that requires multiple independent PRDs — the result is too large or too decomposable for one PRD. The goal is to create just enough GitHub-native structure to sequence feature bets, pick a tracer bullet, and let the selected feature re-enter the normal pipeline with a researchable brief.
One question per turn. When confirming the branch, reviewing bets, or asking the user anything, ask one question at a time and wait for the answer before asking the next. Never batch questions into a list.
Prefer single-select. Use single-select multiple choice when the user is choosing one direction, one priority, or one next step.
Use multi-select rarely. Reserve it for compatible sets — goals, constraints, non-goals, success criteria — that can all coexist. If prioritization matters, follow up asking which selected item is primary.
Use the platform's question tool when available. In Claude Code, use
AskUserQuestion; in Codex,request_user_input; in Gemini,ask_user. Otherwise, present numbered options in chat and wait for the user's reply before proceeding.
Invocation Position
This is a primary planning branch that follows /shape only when the shaped work is too large for a single PRD.
Use /create-milestone when:
/shapealready produced a closing summary- the shaped work requires multiple independent PRDs rather than one bounded feature
- you need to sequence feature bets in GitHub before running feature-level
/research
Do not use it when the work already fits inside one PRD, when a feature is already research-ready, or when the next need is decomposition or implementation.
Why This Exists
/shape can clarify an entire app or product area, but /research needs a narrower question than "how should we build the whole thing?" /create-milestone turns tranche-level understanding into feature-level bets without creating a giant speculative PRD or introducing local roadmap files.
Lifecycle States
Feature issues created by this skill move through three explicit states:
roadmap bet— lightweight feature candidate attached to the milestoneresearch-ready— same issue, expanded enough to support/researchprd— same issue, expanded by/write-a-prdinto the shaped pitch
Do not create a duplicate PRD issue by default. Preserve one canonical feature issue unless the bet fans out so materially that multiple PRDs are clearly required.
Process
1. Confirm the branch
Before creating anything, confirm that the shaped work is too large for a single PRD.
Ask these questions one at a time — ask one, wait for the answer, then proceed to the next:
- What is the milestone or tranche boundary?
- What does success for this tranche look like?
- What is explicitly out of scope for this tranche?
- What is the smallest feature that would prove the architecture or product direction early?
If the answers suggest the work already fits one bounded pitch, do not continue. Return to the normal /research path.
2. Create the milestone frame
Create one GitHub milestone representing the tranche or v1 scope.
The milestone should make these items explicit:
- the tranche name
- the outcome it is trying to produce
- the rough boundary of what is included
- the clearest non-goals
- the milestone-level success criteria
Do not turn the milestone itself into a giant spec. The milestone is the container and progress tracker, not the planning artifact that carries all detail.
3. Derive feature bets
Create a small set of feature issues attached to the milestone.
Keep the set deliberately limited. Create only enough issues to define the tranche clearly and sequence the work. Avoid generating a giant backlog.
Each feature issue should include:
Current maturity: roadmap bet- Problem statement
- User or stakeholder
- Why now
- Appetite
- Dependency notes
- What this feature proves architecturally or product-wise
- Milestone role: tracer bullet, core capability, follow-on, or polish
- Out of scope for now
If labels are available, add a bet label alongside the written maturity field.
4. Sequence by dependency and risk
Order the feature bets using two rules:
- dependency order matters
- the first implemented feature should reveal architectural truth early
Choose one issue as the tracer bullet. It should be the smallest meaningful feature that tests the riskiest integration or core product assumption.
Avoid sequencing by convenience alone. The milestone should burn down uncertainty early, not merely produce visible progress.
5. Promote the next feature to research-ready
When a feature is selected to enter the delivery pipeline, expand that same issue from roadmap bet to research-ready before invoking /research.
The research-ready brief must include:
- Problem
- User or stakeholder
- Success signal
- Why now
- Appetite
- Constraints or impositions
- Assumptions to verify
- What this feature proves
- Out of scope for now
If labels are available, move the issue label from bet to research-ready.
Do not send a raw roadmap bet into /research.
6. Re-entry and backtracking
Once a feature is research-ready, it re-enters the default pipeline at /research, not /shape, because the tranche-level shaping already happened.
Normal progression for that feature becomes:
/research/write-a-prd/prd-to-issues/execute/pre-merge- merge
/compound
If /research shows the milestone-level assumptions were materially wrong, backtrack to /create-milestone or all the way to /shape rather than forcing the work forward.
7. Milestone closeout
When the milestone's feature issues that define the tranche are complete, close the GitHub milestone.
If the tranche taught something reusable beyond a single feature, /compound may capture tranche-level lessons after closeout.
Feature Issue Template
Use this structure for each new milestone feature issue:
<feature-bet-template>Current maturity
roadmap bet
Problem
[What user or business problem this feature addresses]
User / Stakeholder
[Who benefits, who uses it, or who is affected]
Why now
[Why this belongs in this tranche rather than later]
Appetite
[How much time this problem is worth at milestone-planning level]
Dependency notes
[What this likely depends on, if anything]
What this proves
[What architectural, product, or market truth this feature is meant to reveal]
Milestone role
Tracer bullet / Core capability / Follow-on / Polish
Out of scope for now
[Explicit exclusions while this remains a roadmap bet]
</feature-bet-template>Handoff
- Expected input: a closing summary from
/shapefor work that is too large for a single PRD - Produces: a GitHub milestone, sequenced feature issues in
roadmap betstate, and a selected feature promoted toresearch-ready - Re-enters the main pipeline at:
/researchonce a selected feature has been promoted toresearch-ready - May backtrack to:
/shapeif milestone-level decomposition reveals the original shaping was wrong or too coarse
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

