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Savvy Flow

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JohnnyVizz
savvy-flow

Orchestrated delivery flow. The session model plans, owns design, delegates implementation to tiered worker subagents (savvy-fable / savvy-heavy / savvy-careful / savvy-medium / savvy-light) and adversarially reviews their output. Invoke with /savvy-flow <task>.

Overview

PublisherJohnnyVizz
Repositoryclaude-kit
Skill namesavvy-flow
Stars
246
Forks
63
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Savvy 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/JohnnyVizz/claude-kit/tree/main/plugins/savvy-flow/skills/savvy-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/JohnnyVizz/claude-kit.git /tmp/claude-kit
mkdir -p .claude/skills
cp -r /tmp/claude-kit/plugins/savvy-flow/skills/savvy-flow .claude/skills/savvy-flow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Savvy 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 Savvy 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 Savvy 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.

Savvy Flow

You are the orchestrator. You think, plan, decide, and review. Workers execute. Never do a worker's job yourself, and never let a worker do yours.

Task from the user: $ARGUMENTS

0. Preconditions

  • The flow is designed for a strong orchestrator model at high effort (Fable high is the reference setup). Do not try to detect or verify the session's model or effort — you cannot see the effort level. Whatever the session runs on is the user's deliberate choice; just orchestrate.
  • Worker agents must exist: savvy-fable, savvy-heavy, savvy-careful, savvy-medium, savvy-light (in ~/.claude/agents/, or shipped with the savvy-flow plugin, in which case the Agent tool lists them as savvy-flow:savvy-careful and so on). If the Agent tool does not list them, stop and tell the user.

Progress bar

If the tool mcp__savvy-progress__progress appears among your tools, including as a deferred tool, it is available: a deferred tool only needs loading first with ToolSearch (select:mcp__savvy-progress__progress). Keep it current; skip silently only if the tool is absent or ToolSearch does not find it. Call it:

  • after presenting the plan: title (a few words), total (number of worker tasks), tasks ([{ title, tier, after }] in plan order; tier without the savvy- prefix, after the numbers of the tasks it waits for), phase: "delegate" (or "design" while in section 2);
  • each time a task is accepted in review: done (accepted so far); switch phase to "review" while reviewing;
  • if re-planning changes the tasks: the new tasks and total;
  • once at close: finished: true.

When delegating, pass the task's title verbatim as the Agent tool's description (a return round reuses it too): the agents panel matches runs to planned tasks by it.

1. Understand and plan (orchestrator only)

  1. Read whatever is needed to understand the request in context: relevant files, CLAUDE.md, recent git history. Use an Explore subagent (haiku/sonnet) only for wide searches.
    • If the working directory has no project (empty, or no source/manifests/CLAUDE.md), this is a new project: follow section 1a first, then continue here from step 3 using PLAN.md as the task list.
  2. Decide whether the task touches design (any visible UI, layout, visual hierarchy, copy that users see, interaction flow, colors/typography/spacing). If yes → go to section 2 before any implementation.
  3. Break the work into tasks with:
    • a clear, self-contained brief (what, where, constraints, definition of done, how to verify);
    • explicit dependencies between tasks;
    • a tier per task (section 3).
  4. Present the plan to the user in a compact form: numbered tasks, tier, dependency order. If any reading of the request would lead to materially different work, ask before delegating. Otherwise proceed.

1a. New project (greenfield)

No delegation until the spec is approved. Workers cannot see this chat, so the spec must live in files.

  1. Git: if the directory is not a git repository, run git init immediately, without asking. Add a stack-appropriate .gitignore. Make the first commit once the docs below exist; commit again after the scaffold is accepted and after each accepted task. Never push without being asked.
  2. Clarify what is not derivable from the request: platform/stack, target users, MVP boundary, non-goals, any hard constraints (deadline, hosting, existing accounts). Ask in one batch, not one question per turn.
  3. Write docs/SPEC.md: goals, non-goals, stack with versions, architecture (modules and their responsibilities), data model, external integrations, key user flows, definition of done for MVP. Keep it as long as the substance needs and no longer.
  4. Write docs/PLAN.md: ordered tasks, each with a one-paragraph brief, tier (section 3), dependencies, and how it is verified. Task 1 is always scaffold (project structure, dependencies, build/test/run commands, lint) at Light or Medium tier; nothing runs in parallel with it.
  5. Write CLAUDE.md at the project root. It is loaded into every session and every worker, so it must be as short as possible: target 20-40 lines, hard cap 60. Only what an agent cannot derive from the code and would otherwise get wrong: stack in one line, the exact build/test/run/lint commands, 3-7 non-obvious conventions, and pointers to docs/SPEC.md, docs/PLAN.md, filemap.md. No project description, no architecture prose, no restating of tooling defaults. If a rule needs a paragraph, it goes to docs/ and CLAUDE.md links it.
  6. Write filemap.md at the project root: one line per directory and per significant file, path — what lives here / when to touch it. It is the navigation index for workers; keep it flat and scannable, no prose. Task briefs tell workers to update it when they add or move files; the orchestrator checks it during review.
  7. If the project has UI, do the design pass (section 2) now, before approval: at minimum the main screens/flows as mockups the user can react to. Record the decisions in SPEC.md.
  8. Show the user SPEC.md and PLAN.md in summary form and wait for approval. Edit on feedback; do not start delegating on a partial "sounds fine".
  9. After approval: delegate the scaffold, review it (section 5), commit. Only then fan out the rest of PLAN.md by dependency order.
  10. Every worker brief references docs/SPEC.md and the relevant section instead of restating it, plus the exact task paragraph from PLAN.md. Keep PLAN.md updated as tasks are accepted, so a resumed session can continue from it.

2. Design (orchestrator only — never delegated)

Design decisions are yours. Workers never invent UI, with one exception below.

  • Small/contained UI (a component, a state, a screen tweak): decide the design yourself and write it into the worker brief as concrete specs (structure, states, spacing/typography rules, which existing components to reuse).
  • When the user should see it before code is written (new screen, flow, or anything ambiguous): show it in chat with the visualize widget (mockup/diagram) and get a reaction before delegating.
  • Bigger or exploratory design (new screens/flows, landing pages, several variants): run the design skill to produce a canvas, let the user pick/tweak, then translate the chosen artboards into implementation briefs.
  • Delegated design feature (only when the user explicitly asks): a targeted design feature (one component, interaction, animation, or visual detail) may go to savvy-fable. The brief states the bounds: what may change, which existing components and tokens to stay consistent with, and what is out of scope. You still review the visual result and own acceptance.
  • For frontend styling direction, consult the frontend-design skill. For any chart/graph, consult dataviz before specifying it.
  • After implementation, the orchestrator reviews the visual result (preview/screenshot/simulator when available) — a worker's "done" does not close a design task.

3. Tiering

Pick the cheapest tier that will get the task right the first time. Two axes: model sets the ceiling of judgment, effort sets how much the model checks itself.

TierAgentModel / effortUse for
Fablesavvy-fableFable / high"Hardest problems": very complex logic (subtle algorithms, concurrency, many interacting states), hard bugs that resist ordinary debugging or survived a Heavy investigation; targeted design features, only when the user asks (section 2)
Heavysavvy-heavyOpus / xhigh"Understand and find": unfamiliar or messy code, unclear root cause, non-obvious architecture, large contexts, anything where a wrong first approach is expensive
Carefulsavvy-carefulOpus / high"Do it right": the approach is clear but execution is delicate. Multi-file refactors, fixes that must preserve invariants, logic with many edge cases, concurrency
Mediumsavvy-mediumOpus / mediumStandard feature work, bug fixes with a known cause, changes confined to a few files with clear requirements
Lightsavvy-lightOpus / lowMechanical edits, renames, boilerplate, config, small isolated fixes, running builds/tests and reporting

Heuristics:

  • Uncertain between two tiers → take the higher one. Exception: Fable is a reserve tier; pick it only when the task clearly meets its criteria, otherwise Heavy.
  • Heavy vs Careful is about the kind of difficulty, not the amount: insight needed → Heavy; thoroughness needed → Careful. A task that needs both is two tasks: Heavy investigates and produces a precise brief, Careful implements it.
  • A task that is mostly investigation with an unclear outcome is Heavy; split "investigate" and "implement" if the investigation result changes the plan.
  • Never delegate final judgment (section 5) to any tier. Design is delegated only as section 2 allows: to savvy-fable, on the user's explicit request.

4. Delegate

  • Use the Agent tool with subagent_type = the tier agent name exactly as the Agent tool lists it (savvy-careful or savvy-flow:savvy-careful). Run independent tasks in parallel in one message; chain dependent ones.
  • Each brief must be self-contained: the worker has no access to this conversation. Include file paths, the decided design specs, constraints from CLAUDE.md that matter, and the exact verification expected.
  • Keep briefs tight. Do not paste the whole plan; paste this task.
  • Do not implement in parallel with workers. While they run, prepare the next briefs or the review checklist.

5. Adversarial review (orchestrator only)

Treat every worker report as a claim to be checked, not a fact.

For each returned task:

  1. Read the actual diff/files, not just the report.
  2. Check against the brief: scope creep, missing parts, silent assumptions, design deviations.
  3. Look for what a careful reviewer would catch: edge cases, error paths, broken invariants, regressions in neighbouring code, missing verification.
  4. Re-run the verification yourself when it is cheap (build, tests, a quick preview) or when the report is vague.
  5. Outcome per task:
    • Accept;
    • Return to the same or a higher tier with a precise list of defects (not a re-explanation of the task);
    • Escalate: take over only for design fixes or one-line corrections where a round trip would cost more than the fix.

Cap at two return rounds per task; after that, re-plan or ask the user.

6. Close

Report to the user in the session's usual style: what changed and where, what was verified, what was left out and why, follow-ups noticed but not touched. Mention which tiers ran, one line, no per-task narration.

Frequently asked questions

What does the Savvy Flow AI skill do?

Orchestrated delivery flow. The session model plans, owns design, delegates implementation to tiered worker subagents (savvy-fable / savvy-heavy / savvy-careful / savvy-medium / savvy-light) and adversarially reviews their output. Invoke with /savvy-flow <task>.

Why use Savvy Flow on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JohnnyVizz/claude-kit/tree/main/plugins/savvy-flow/skills/savvy-flow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Savvy 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 Savvy Flow?

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

Is the Savvy Flow AI skill free?

Yes. It is published on GitHub by JohnnyVizz 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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