App Spec Packager
instructa/agent-skills
A skill your agent uses when the user wants to turn an application, product, startup idea, SaaS, mobile app, web app, API, AI product, or internal tool into a production-ready Markdown specification…
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.
$ npx skills add shep-ai/shep --skill shep-workstreams -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install shep-ai/shep shep-workstreams --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "shep-workstreams" agent skill from https://github.com/shep-ai/shep/tree/main/.claude/skills/shep-workstreams into .claude/skills/shep-workstreams/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "shep-workstreams", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/shep-ai/shep/tree/main/.claude/skills/shep-workstreamsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add shep-ai/shep --skill shep-workstreams -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install shep-ai/shep shep-workstreams --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shep-ai/shep.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/shep-workstreams .agents/skills/shep-workstreams && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "shep-workstreams" agent skill from https://github.com/shep-ai/shep/tree/main/.claude/skills/shep-workstreams into .agents/skills/shep-workstreams/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "shep-workstreams", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add shep-ai/shep --skill shep-workstreams -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install shep-ai/shep shep-workstreams --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shep-ai/shep.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/shep-workstreams .cursor/skills/shep-workstreams && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "shep-workstreams" agent skill from https://github.com/shep-ai/shep/tree/main/.claude/skills/shep-workstreams into .cursor/skills/shep-workstreams/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "shep-workstreams", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/shep-ai/shep.git --path .claude/skills/shep-workstreams--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add shep-ai/shep --skill shep-workstreams -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install shep-ai/shep shep-workstreams --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shep-ai/shep.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/shep-workstreams .gemini/skills/shep-workstreams && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "shep-workstreams" agent skill from https://github.com/shep-ai/shep/tree/main/.claude/skills/shep-workstreams into .gemini/skills/shep-workstreams/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "shep-workstreams", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install shep-ai/shep shep-workstreamsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add shep-ai/shep --skill shep-workstreams -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/shep-ai/shep.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/shep-workstreams .github/skills/shep-workstreams && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "shep-workstreams" agent skill from https://github.com/shep-ai/shep/tree/main/.claude/skills/shep-workstreams into .github/skills/shep-workstreams/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "shep-workstreams", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add shep-ai/shep --skill shep-workstreams -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install shep-ai/shep shep-workstreams --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shep-ai/shep.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/shep-workstreams .opencode/skills/shep-workstreams && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "shep-workstreams" agent skill from https://github.com/shep-ai/shep/tree/main/.claude/skills/shep-workstreams into .opencode/skills/shep-workstreams/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "shep-workstreams", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
shep-workstreamsA 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. 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.
2 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 0304cfc. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
gitFrom the folder's file list and the shell code blocks in SKILL.md.
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.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from shep-ai/shep at commit 0304cfc, republished under its MIT licence (© shep-ai). 1,092 words, ~2,543 tokens.
.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.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.
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.
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.
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.
Answer these explicitly in the plan. They are the whole point of the exercise:
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.
WORKSTREAMS.mdUse templates/workstream-plan.md. Every workstream gets, without exception:
| Field | Meaning |
|---|---|
scope | What is in it — and an explicit not in scope line |
blocked_by / blocks | Edges of the dependency graph, by workstream id |
expected_files | Globs. If two workstreams share a glob, one of them is mis-cut |
merge_risk | Low / Medium / High, with the reason |
branch | Recommended branch name |
completion_criteria | Observable, checkable — not "done" |
shep_command | The 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).
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.
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.
Create features in dependency order so --parent ids exist when you need them.
# 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)# 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# 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# 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>| Situation | Use |
|---|---|
| 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 capacity | plain shep feat new — run it now |
| B and A edit the same files | Not 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.
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.
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 agentMerge 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.
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.
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
SKILL.md and 3 other files (references) in .claude/skills/shep-workstreams of shep-ai/shep.
Open the folder on GitHubat commit 0304cfc
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Shep Workstreams this skillshep-ai/shep | 264 | — | ~2.5k | Automated safety check: Pass | MIT | |
| App Spec Packagerinstructa/agent-skills | 139 | — | ~1.5k | Automated safety check: Pass | None | |
| Squid Planiusztinpaul/squid | 203 | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| CCPM Project Managementautomazeio/ccpm | 8.4k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Schematicblader/schematic | 240 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Execute Fleetanombyte93/prd-taskmaster | 605 | — | ~2.2k | Automated safety check: Notes | MIT |
instructa/agent-skills
A skill your agent uses when the user wants to turn an application, product, startup idea, SaaS, mobile app, web app, API, AI product, or internal tool into a production-ready Markdown specification…
iusztinpaul/squid
Turn a raw feature spec into an approved Tasks Plan — grill the spec, have the Product Architect groom draft tasks (+ optional ADR and glossary additions), then run ONE human gate that decides…
automazeio/ccpm
Runs a spec-driven workflow from PRD to epic to GitHub issues to parallel agents, with status, standup and blocked-work reports from bundled scripts.
blader/schematic
Reverse engineer a detailed product and technical specification document from a git branch's implementation.
anombyte93/prd-taskmaster
Phase execution skill for licensed Atlas Fleet runs. An agent skill from anombyte93/prd-taskmaster.
pingcap/docs
Write new TiDB documentation or update existing TiDB documentation from code changes, PRs, issues, design docs, product specs, rough drafts, existing docs, or short feature descriptions.
shep-ai/shep
React Flow (@xyflow/react) for workflow visualization with custom nodes and edges.
shep-ai/shep
A skill your agent uses when making architectural decisions, planning features, designing new components, reviewing PRs, or validating that proposed changes align with Clean Architecture principles.
shep-ai/shep
Cross-validate documentation and artifacts across the codebase for consistency, conflicts, and contradictions.
shep-ai/shep
A skill your agent uses when creating, modifying, or documenting TypeSpec domain models.
Categories
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.
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.
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.
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.
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.
Going by SKILL.md and its folder, Shep Workstreams needs the command-line tools its instructions call (git).
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.
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.
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.
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.
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.
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.