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Flow

Organization
Lifecycle-Innovations-Limited
flow

OPS on-demand: This skill should be used when the user asks to "/ops:flow", "dev lifecycle", or "where…

Overview

PublisherLifecycle-Innovations-Limited
Repositoryclaude-ops
Skill nameflow
Stars
543
Forks
64
Bundled files
1
LicenseMIT
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 Lifecycle-Innovations-Limited on GitHub. Read the source before you install it.

Installation

Install the Flow 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.
    https://github.com/Lifecycle-Innovations-Limited/claude-ops/tree/main/claude-ops/skills/flow
  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/Lifecycle-Innovations-Limited/claude-ops.git /tmp/claude-ops
mkdir -p .claude/skills
cp -r /tmp/claude-ops/claude-ops/skills/flow .claude/skills/flow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Flow 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 Flow 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 Flow 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.

Agent Teams support

If CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is set, use Agent Teams when a lifecycle stage fans out into parallel, coordinated work (e.g. build + test + review running together, or a multi-repo ship). This enables:

  • Stage agents share context mid-flight (the test agent surfaces a regression → the build agent pivots before review starts)
  • You can steer priorities in real time ("land the API change first, then docs")
  • Agents report progress as each stage completes, so you sequence the merge

Team setup (only when the flag is enabled, and only for genuinely parallel stages):

TeamCreate("flow-lifecycle")
Agent(team_name="flow-lifecycle", name="stage-build", ...)
Agent(team_name="flow-lifecycle", name="stage-test", ...)

Steer with SendMessage / broadcast; share work via TaskCreate/TaskUpdate.

If the flag is NOT set, fall back to standard fire-and-forget subagents (the default), or just route the stage to its single canonical command inline.

Runtime Context

Before routing, compute where the user is on the lifecycle:

bash
# FLOW_TARGET: the project you are actually working on. Default to the shell's
# cwd, but pass an explicit path when the harness's tool cwd is not the project
# (see the mode-resolution note below).
FLOW_TARGET="${FLOW_TARGET:-$PWD}"
FLOW_STATE=""
for _d in "${CLAUDE_PLUGIN_ROOT:-}" \
          "$HOME/Developer/repos/claude-ops/claude-ops" \
          "$HOME/external-skills/claude-ops" \
          "$HOME/.claude/plugins/marketplaces/ops-marketplace/claude-ops" \
          "$HOME/Projects/claude-ops/claude-ops"; do
  [ -n "$_d" ] && [ -x "$_d/bin/flow-state" ] && { FLOW_STATE="$_d/bin/flow-state"; break; }
done
[ -n "$FLOW_STATE" ] || { echo "flow: no executable bin/flow-state found" >&2; exit 1; }
"$FLOW_STATE" "$FLOW_TARGET"

Both failures are fail-closed on purpose. If no helper resolves, or the target path is not a directory, stop and say so — do not route. MODE is what decides GSD vs gstack, so routing without it silently picks the wrong half of the lifecycle, which is worse than an error.

Use ${CLAUDE_PLUGIN_ROOT:-}, not $CLAUDE_PLUGIN_ROOT: the bare form aborts under set -u, and an unguarded ${CLAUDE_PLUGIN_ROOT}/bin/flow-state expands to the absolute path /bin/flow-state when the variable is unset.

Always pass the target path explicitly. flow-state defaults to $PWD, and on some harnesses the shell tool does NOT run in the directory the user launched from — Hermes, for example, runs terminal commands in $HOME. A bare flow-state there inspects the home directory, finds no .planning/, and reports MODE=AD-HOC for every project. PROJECT-MODE then never triggers, so the GSD phase state machine is silently unreachable and every request falls through to the ad-hoc gstack lifecycle. Resolve the project directory first (the repo the user named, or the one under discussion) and pass it.

This prints: mode (PROJECT / AD-HOC), current .planning/ phase (if any), open PRs, and deploy state — the "you are here" marker. Read it first so mode-sensitive routes (build / ship / review) resolve correctly.

The canonical map lives in LIFECYCLE-MAP.md (same dir). Read it when you need the full per-stage ownership table; the dispatch table below is the routing copy.


FLOW — One Lifecycle Entrypoint

Load ops-rules before acting. Public repo (no personal data). Outbound: one draft → one approval → one send. If AskUserQuestion / Workflow are missing, follow Rule 10 in ops-rules (Hermes: numbered options / two-turn Telegram card; delegate_task).

The one rule: pick the abstraction level from repo state, then delegate to the canonical tool.

  • PROJECT-MODE (git root has .planning/): drive the GSD phase state machine; GSD phases call gstack/ops tools as sub-steps.
  • AD-HOC-MODE (no .planning/): run the gstack stateless lifecycle.
  • OPS (/ops:*): always available in either mode.

Route $ARGUMENTS (first token = intent) using this table:

Intent keywordsResolves to
(empty), map, here, whereprint map (Read LIFECYCLE-MAP.md) + bin/flow-state output. Stop — do not delegate.
ideate, brainstorm, office-hours/office-hours
hard-truths, yolo/ops:ops-yolo
spec, specify, scope, issue/spec $REST
plan, roadmap, phase-planproject → gsd-plan-phase; ad-hoc → /autoplan
ultraplan, deep-plangsd-ultraplan-phase
design, ui, mockup, html/design-consultation $REST
build, execute, implement, codemulti-project (checked FIRST) → gsd-manager; else project → gsd-execute-phase; ad-hoc → direct edits in an isolated worktree
feature-dev, fd, feature, architect-feature/feature-dev $REST (overlay — optional structured pipeline before/alongside build; does not replace GSD execute)
review, code-review, cr/review — project also runs gsd-code-review
security, cso, sec-review/cso
test, qa/qa $REST — project also runs gsd-verify-work
ios-qa, ios-test/ios-qa
ship, landproject → gsd-ship; ad-hoc single-repo → /ship; multi-repo salvage → /ops:ops-merge
deploy, release, rollout/ops:ops-deploy then /canary
canary/canary
monitor, fires, incidents/ops:ops-fires
status, health/ops:ops-status
retro, reflect/retro
learn/learn
ops, inbox, comms, marketing, finops, voice, home, daemon/ops:ops $ARGUMENTS (hand the whole arg string to the ops sub-router)
projects, portfolio/ops:ops-projects
debug, investigate, root-cause, why/investigate — project also runs gsd-debug
explore, onboard, understand, codebaseproject → gsd-map-codebase / gsd-onboard; ad-hoc → gsd-explore
spike, prototype, trygsd-spike (throwaway, never lands)
docs, document, readme/document-generate — project also gsd-docs-update; post-ship /document-release
diagram, chart, excalidraw, mermaid/diagram
pdf, export/make-pdf
scrape, extract, pull-data/scrape (codify a repeat flow with /skillify)
save-context, park, resume/context-save / /context-restore — project → gsd-pause-work / gsd-resume-work
freeze, guard, scope-edits/freeze (dir scope), /guard (full), /careful (destructive-cmd warnings), /unfreeze
design-review, ux-audit/design-review; iOS → /ios-design-review; devex → /devex-review
ios-fix, ios-clean, ios-sync/ios-fix, /ios-clean, /ios-sync
benchmark, perf, regression/benchmark

Routing notes

  • Hermes: gstack targets are files, not skills. Every gstack route below (/qa, /ship, /spec, /review, /cso, /autoplan, /canary, /retro, /learn, /office-hours, /design-consultation, /ios-qa, /browse) lives OUTSIDE the indexed skills tree at ~/.local/share/gstack/<name>/SKILL.md, deliberately, so its ~950k tokens do not load into every context. skill_view("qa") WILL fail. Load the route with read_file ~/.local/share/gstack/<name>/SKILL.md, then follow it. The gstack-index skill lists all 54 names. GSD routes are normal skills and do resolve via skill_view (gsd-plan-phase, gsd-execute-phase, ...).
  • $REST = $ARGUMENTS with the leading intent token removed.
  • Mode resolution: use the MODE= line from flow-state, and pass it the PROJECT path (see Runtime Context). PROJECT when the git root has .planning/; AD-HOC otherwise. If you did not pass a path and the harness runs shell commands somewhere other than the project (Hermes uses $HOME), MODE is meaningless — it describes that directory, not the user's work. A universal AD-HOC across unrelated projects is the symptom. "multi-project" = the user named >1 repo/project or asked for portfolio-wide work.
  • Delegation only. This skill never does the work itself — it invokes the canonical command via the Skill tool (or prints the route if the target is a personal/plugin slash-command the model should call next). After routing, the target skill's own ## CLI/API Reference governs execution.
  • Ambiguity: if intent spans two stages, prefer the earliest unfulfilled stage for the current flow-state position.
  • ops passthrough: for any ops* intent, forward the entire $ARGUMENTS to /ops:ops — it has its own sub-router; do not pre-parse it.

Bare /flow

If $ARGUMENTS is empty: Read LIFECYCLE-MAP.md, then reuse the Runtime Context result above (the same "$FLOW_STATE" "$FLOW_TARGET" invocation — do not call bin/flow-state bare, it would report on the harness's cwd rather than the project), and present the lifecycle map with the current position highlighted. Offer the next canonical stage as the suggested action. Do not auto-advance.

CLI/API Reference

Router only — no direct tool calls. bin/flow-state is the sole helper (detects mode + position). All execution is delegated to the canonical target skill after routing.

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 Flow AI skill do?

OPS on-demand: This skill should be used when the user asks to "/ops:flow", "dev lifecycle", or "where…

Why use Flow on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Lifecycle-Innovations-Limited/claude-ops/tree/main/claude-ops/skills/flow. 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 Flow?

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 Flow?

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

Is the Flow AI skill free?

Yes. It is published on GitHub by Lifecycle-Innovations-Limited under the MIT 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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