Agent skill

Shep Workstreams

by shep-ai in shep-ai/shep

A skill your agent uses when a large body of work (a version milestone, an epic, a roadmap, a set of PRDs/design docs) needs to be broken into parallel workstreams and executed with the shep CLI.

MITAuto-check passedProduct & Project Management

Install Shep Workstreams

skills CLI
$ npx skills add shep-ai/shep --skill shep-workstreams -a claude-code

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

GitHub CLI
$ gh skill install shep-ai/shep shep-workstreams --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/shep-ai/shep.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/shep-workstreams .claude/skills/shep-workstreams && 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
shep-workstreams
GitHub stars
264
Token cost
~2.5k tokens
SKILL.md length
1,092 words
Files
4 (incl. references)
Skills in repo
5
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when a large body of work (a version milestone, an epic, a roadmap, a set of PRDs/design docs) needs to be broken into parallel workstreams and executed with the shep CLI.

  • Works in 2 steps: Produce the workstream plan → Execute with the shep CLI
  • A large body of work (a version milestone
  • SKILL.md covers The rule this skill exists to…, Two phases, hard separated, Phase 1 — Produce the… and Phase 2 — Execute with the…, plus 1 more section
  • Calls git

What it does

Shep Workstreams is an agent skill from shep-ai/shep. Use when a large body of work (a version milestone, an epic, a roadmap, a set of PRDs/design docs) needs to be broken into parallel workstreams and executed with the shep CLI. Triggers include "break this down", "split into workstreams", "plan V6", "what should we build first", "run these in parallel", "dependency graph", "merge order", "worktree split", or any request to turn planning docs into running shep features. Produces a workstream plan first, then drives shep feat new / shep feat start wave by wave. Part…

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/cli-reference.md`, `references/partitioning.md` and `templates/workstream-plan.md`).

It sits in Product & Project Management, covering Git worktrees, Architecture decision records and Project management. The repository describes itself as: Ship features 10x faster. Built In Auto: Memory, K8S Agent & Security (SDD+SDLC) . 😇. The licence is MIT.

When your agent uses it

  • A large body of work (a version milestone
  • A set of PRDs/design docs) needs to be broken into parallel workstreams and executed with the shep CLI
  • Include break this down
  • Split into workstreams

Example prompts

  • “break this down”
  • “split into workstreams”
  • “plan V6”
  • “/shep-workstreams”

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. Produce the workstream plan
  2. Execute with the shep CLI

What it can do on your machine

Read from SKILL.md and the folder at commit 0304cfc. 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

    Shell commands in SKILL.md call:

    • git

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

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    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

Shep Workstreams loads about 2.5k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 149 tokens; SKILL.md has 1,092 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~149
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.2k

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 shep-ai/shep at commit 0304cfc, republished under its MIT licence (© shep-ai). 1,092 words, ~2,543 tokens.

Download SKILL.mdSave it as .claude/skills/shep-workstreams/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
shep-workstreams
description
Use when a large body of work (a version milestone, an epic, a roadmap, a set of PRDs/design docs) needs to be broken into parallel workstreams and executed with the shep CLI. Triggers include "break this down", "split into workstreams", "plan V6", "what should we build first", "run these in parallel", "dependency graph", "merge order", "worktree split", or any request to turn planning docs into running shep features. Produces a workstream plan first, then drives `shep feat new` / `shep feat start` wave by wave. Part of the Shep autonomous SDLC platform — https://shep.bot
metadata.version
1.0.0
metadata.author
Shep AI (https://shep.bot)
metadata.homepage
https://shep.bot
metadata.repository
https://github.com/shep-ai/shep

Workstream Breakdown & Parallel Execution with Shep

Turn a large, vague body of work into a small set of parallel workstreams, then execute each one as an isolated shep feature. One workstream = one feature = one worktree = one agent.

The rule this skill exists to enforce

Never tell an agent "work on all of it." That produces context loss, jumping between unrelated tasks, dozens of touched files, giant commits, and merge conflicts.

Act like a tech lead: partition first, then run one focused agent per partition.

Two phases, hard separated

PHASE 1 — PLAN            PHASE 2 — EXECUTE
(read-only, no shep       (shep feat new / start,
 mutations, produces      wave by wave, monitored)
 WORKSTREAMS.md)

Do not create a single feature until Phase 1 is written down and the user has approved it. The plan is cheap. Rework across six half-merged branches is not.


Phase 1 — Produce the workstream plan

Step 1: Read every source doc, in full

Read all the input material (PRDs, design docs, roadmap, existing specs, issue lists) before writing anything. Then inventory the deliverables — not the docs, the actual things that must exist when this is done.

Step 2: Answer the five tech-lead questions

Answer these explicitly in the plan. They are the whole point of the exercise:

  1. What is on the critical path? Which deliverables block everything else, and which are genuinely independent?
  2. What does the dependency graph look like? Which files and components are shared between deliverables? Which subtrees are touched by exactly one deliverable?
  3. What is the right worktree split? Group deliverables into the smallest set of parallel branches that minimises merge conflicts. Estimate merge risk for each.
  4. What are the integration milestones? Replace "finish V6" with contract-level checkpoints ("backend contract frozen", "design system frozen", "CRUD complete", "QA").
  5. Which decisions are irreversible? What should be decided now because changing it later is expensive — and what should be deliberately delayed behind a flag?
Step 3: Partition on file ownership, not on feature semantics

This is the single highest-leverage rule, and the one most often gotten wrong:

Two deliverables that both edit the same file belong in the same workstream, no matter how unrelated they sound. Two deliverables that touch disjoint subtrees belong in different workstreams, no matter how related they sound.

For the full partitioning rubric — the foundation-first rule, ship-the-small-thing-first rule, merge-risk scoring, concurrency caps, and the common failure modes — read references/partitioning.md.

Step 4: Write WORKSTREAMS.md

Use templates/workstream-plan.md. Every workstream gets, without exception:

FieldMeaning
scopeWhat is in it — and an explicit not in scope line
blocked_by / blocksEdges of the dependency graph, by workstream id
expected_filesGlobs. If two workstreams share a glob, one of them is mis-cut
merge_riskLow / Medium / High, with the reason
branchRecommended branch name
completion_criteriaObservable, checkable — not "done"
shep_commandThe exact shep feat new invocation that starts it

Then add, at the plan level: the merge order, the integration milestones, the irreversible decisions, and any work that should be pulled earlier into the current release (small, high-impact, zero-dependency items usually should be).

Step 5: Stop and get approval

Present the plan. Ask specifically about: the workstream count, the merge order, and anything recommended for pull-forward. Do not proceed to Phase 2 unapproved.


Phase 2 — Execute with the shep CLI

The mechanics that matter

Shep already does worktree-per-workstream. Do not hand-roll git worktree add.

  • shep feat new "<description>" creates a branch and an isolated worktree off the repo's default branch, then spawns an agent in it.
  • --pending creates the feature without spawning. shep feat start <id> spawns it later. This is how you stage waves.
  • --parent <feature-id> records a real dependency. The child starts Blocked. When the parent completes — Maintain, i.e. its branch actually merged — shep automatically rebases the child's branch onto the parent's work and spawns its agent. Implementation and Review do not release the child: that code is still being rewritten, and a PR under review may never land. Only direct children unblock — a chain A → B → C cascades one link at a time, which is what you want.
  • --attach <path> is repeatable. Attach the source PRDs/design docs to every feature so each agent has the context without you pasting it.

Full flag reference, monitoring commands, and the PM/work-item commands are in references/cli-reference.md. Read it before composing commands.

Show full SKILL.md (401 more words)Show less
Step 1: Encode the graph, do not improvise it

Create features in dependency order so --parent ids exist when you need them.

bash
# Wave 0 — foundation. Nothing else can start until this one is merged.
shep feat new "Foundation: shared types, API contracts, routing, feature flags, design tokens" \
  --repo /path/to/project \
  --attach docs/v6-overview.md --attach docs/v6-api.md \
  --push --pr

# Note the returned feature id (or: shep feat ls)
bash
# Wave 1 — dependents. Blocked until the foundation merges, then auto-rebased + spawned.
shep feat new "Creator storefront: creator page, claim flow, generated profile" \
  --repo /path/to/project --parent <foundation-id> \
  --attach docs/v6-storefront.md --push --pr

shep feat new "Listings: listing page, CRUD, publish flow, ownership badges" \
  --repo /path/to/project --parent <foundation-id> \
  --attach docs/v6-listings.md --push --pr
bash
# Truly independent, zero shared files — start it immediately, in parallel with foundation.
shep feat new "Attribution: deep links, ?via= params, tracking, creator analytics" \
  --repo /path/to/project --attach docs/v6-attribution.md --push --pr
bash
# Later waves you want held back for capacity, not dependencies — stage them.
shep feat new "Collections: object model, relationships, recommendation surfaces" \
  --repo /path/to/project --parent <foundation-id> --pending
# release when you have the reviewer bandwidth:
shep feat start <collections-id>
Step 2: Choose the dependency mechanism deliberately
SituationUse
B needs A's code--parent <A> — auto-rebase onto A's branch, auto-start
B is independent but you lack review capacity now--pending, then shep feat start
B is independent and you have capacityplain shep feat new — run it now
B and A edit the same filesNot two workstreams. Merge them into one feature

--parent is not a substitute for good partitioning. If everything is a child of everything, you have built a sequential pipeline with extra steps.

Step 3: Respect the concurrency cap

Run at most as many in-flight features as you have disjoint file sets — and no more than you can actually review. Six agents producing six unreviewed PRs is not parallelism, it is a queue with a worse failure mode. Stage the rest with --pending.

Step 4: Monitor and drive to merge order
bash
shep feat ls                 # tree: repo → feature → children, with lifecycle + phase
shep feat show <id>          # detail, including what it is waiting on
shep feat logs <id>          # agent output
shep feat approve <id>       # clear an approval gate
shep feat reject <id>        # send it back with feedback
shep feat resume <id>        # resume a stopped or failed feature agent

Merge in the plan's stated order. After each merge, re-check shep feat ls — the merge is what moves the parent to Maintain, which unblocks its children automatically, and their rebases may have surfaced conflicts worth looking at before the next wave.

Step 5: Track the graph as work items (optional, for larger efforts)

When the effort is big enough that the plan needs to outlive the terminal session, mirror it in shep's PM layer — shep project new, shep item new, shep item relate --type blocking, shep cycle new, shep cycle add-items. See references/cli-reference.md.


Anti-patterns — reject these when you see them

  • One mega-feature. "Implement V6" is not a workstream. If the description needs the word "and" three times, split it.
  • Workstreams cut along team or doc boundaries. Cut along file boundaries. The doc structure has no idea what shares a schema file.
  • Everything parented to everything. That is a sequential pipeline. Find the genuinely independent slice and run it now.
  • Foundation that keeps growing. Foundation is contracts, types, routing, flags, tokens — and nothing else. The moment it grows a UI, it stops being mergeable and blocks five streams.
  • Deferring the small high-impact slice. If something is small, near-zero-dependency, and valuable, ship it first. It validates the pipeline end to end while foundation is in flight.
  • Hand-rolled git worktree add alongside shep features. Shep owns the worktrees. Mixing the two loses tracking, gates, auto-rebase, and unblocking.

© shep-ai, 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 3 other files (references) in .claude/skills/shep-workstreams of shep-ai/shep.

  • SKILL.md
  • references/cli-reference.md
  • references/partitioning.md
  • templates/workstream-plan.md

Open the folder on GitHubat commit 0304cfc

Compare with similar skills

Shep Workstreams 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.

Shep Workstreams compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Shep Workstreams this skillshep-ai/shep264—~2.5kAutomated safety check: PassMIT
App Spec Packagerinstructa/agent-skills139—~1.5kAutomated safety check: PassNone
Squid Planiusztinpaul/squid203—~2.6kAutomated safety check: PassApache-2.0
CCPM Project Managementautomazeio/ccpm8.4k—~1.1kAutomated safety check: PassMIT
Schematicblader/schematic240—~2.2kAutomated safety check: PassMIT
Execute Fleetanombyte93/prd-taskmaster605—~2.2kAutomated safety check: NotesMIT

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Questions about Shep Workstreams

What does Shep Workstreams do?

A skill your agent uses when a large body of work (a version milestone, an epic, a roadmap, a set of PRDs/design docs) needs to be broken into parallel workstreams and executed with the shep CLI. Shep Workstreams is an agent skill from shep-ai/shep. Use when a large body of work (a version milestone, an epic, a roadmap, a set of PRDs/design docs) needs to be broken into parallel workstreams and executed with the shep CLI.

When should I use Shep Workstreams?

Shep Workstreams fits situations like: A large body of work (a version milestone; A set of PRDs/design docs) needs to be broken into parallel workstreams and executed with the shep CLI; include break this down; split into workstreams.

How do I install Shep Workstreams in Claude Code?

Run `npx skills add shep-ai/shep --skill shep-workstreams -a claude-code`. Or copy the skill folder (.claude/skills/shep-workstreams in shep-ai/shep) into .claude/skills/shep-workstreams in your project. Claude Code loads it when a task matches its description.

How do I install Shep Workstreams in Codex?

Run `npx skills add shep-ai/shep --skill shep-workstreams -a codex`. Or copy the skill folder (.claude/skills/shep-workstreams in shep-ai/shep) into .agents/skills/shep-workstreams in your project. Codex loads it when a task matches its description.

Can I use Shep Workstreams 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 shep-ai/shep --skill shep-workstreams -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/shep-workstreams, .gemini/skills/shep-workstreams, .github/skills/shep-workstreams and .opencode/skills/shep-workstreams in your project.

What does Shep Workstreams need to run?

Going by SKILL.md and its folder, Shep Workstreams needs the command-line tools its instructions call (git).

Does Shep Workstreams access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Shep Workstreams 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 Shep Workstreams use?

Shep Workstreams 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 Shep Workstreams use?

About 2.5k tokens (SKILL.md is roughly 10k 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 3.6k tokens, read only when the agent opens those files.

What are the alternatives to Shep Workstreams?

Skills that share tags, products or a category with Shep Workstreams: App Spec Packager (instructa/agent-skills, 139 stars), Squid Plan (iusztinpaul/squid, 203 stars), CCPM Project Management (automazeio/ccpm, 8.4k stars) and Schematic (blader/schematic, 240 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Shep Workstreams?

shep-ai (a GitHub organization) maintains it in shep-ai/shep, which has 264 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 8, 2026.

Source: shep-ai/shep on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.