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Make It Flow

Community
MrBlushu
make-it-flow

Select, compare, compose, specify, implement, and verify interaction patterns for screen-based interfaces. Use when choosing or revising navigation, screen structure, workflows, list/detail behavior, action and state models, forms, data exploration, feedback and recovery, responsive adaptations, or whether an implementation preserves interaction state, especially when alternatives have meaningful tradeoffs or behavior is unresolved. Do not use for product discovery, generic usability audits without a pattern decision, purely aesthetic polish, text-only editing, accessibility certification, unrelated debugging, or mechanical implementation of an already complete interaction specification.

Overview

PublisherMrBlushu
Repositoryblushu-design-skills
Skill namemake-it-flow
Stars
6
Forks
0
Bundled files
7
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.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Make It 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/MrBlushu/blushu-design-skills/tree/main/plugins/blushu-design-skills/skills/make-it-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/MrBlushu/blushu-design-skills.git /tmp/blushu-design-skills
mkdir -p .claude/skills
cp -r /tmp/blushu-design-skills/plugins/blushu-design-skills/skills/make-it-flow .claude/skills/make-it-flow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Make It 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 Make It 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 Make It 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.

Make It Flow

Turn a concrete interface problem into a coherent, testable interaction model. Implement only when the user explicitly requests a build or change; keep review and advice read-only.

Resolve the execution profile

Resolve this before loading references or inspecting the project or artifact:

  • create: produce a new interaction decision, specification, or blueprint; write only when the task authorizes an artifact;
  • change: revise or implement an existing interaction within the named scope, but write only with separate task-supplied authorization;
  • review: inspect a supplied interaction for evidence-backed mismatches without modifying files;
  • verify: test only declared states, transitions, navigation, input, feedback, recovery, or continuity criteria on completed work; always remain read-only.

Map the previous design mode to create, and revision or implementation to change; implementation still requires explicit write authorization. Treat a focused decision as a scope modifier. Choose the profile from an explicit token after the skill name, then an explicit handoff Mode, then unambiguous request language, otherwise preserve the existing design/review behavior.

After resolving any profile, require a concrete interaction task or question, usable scope or artifact, and any authority the profile needs. If the request or workflow handoff supplies only the skill name, profile token, or Mode without that task context, and no usable recent context resolves it, return only the resolved Mode or Mode: unresolved, Status: NEEDS_TASK, the missing task fields, and Mutations: none; then stop before inspecting artifacts, loading references, or routing work.

For a workflow verify handoff, require Question, Scope, Baseline, Criteria, Authority: read-only, Locked Decisions, Invocation ID, Artifact Revision, and Pass Limit: 1. Recover a direct conversational task from the immediately preceding context only when artifact, closed question, scope, baseline, and criteria have one strongly supported interpretation. Otherwise return only Mode: verify, Status: NEEDS_TASK, the missing fields, and Mutations: none.

In verify, check only declared criteria and directly affected neighboring state in one bounded pass. Use real behavior when the claim requires it and load only the relevant domain and verification references. Retry one tool action only for a clearly transient safe failure. Do not mutate files, designs, configuration, or external systems; do not compare new pattern candidates, redesign the flow, or recommend or apply a repair; do not invoke another skill; and do not execute a handoff or Next Task.

End verify with Mode, terminal Status: PASS | FAIL | BLOCKED, checked Scope, criterion-specific Evidence, Failed Criteria, optional uninvestigated Out-of-scope Signals, Owner for a failure, and Mutations: none. PASS needs evidence for every criterion; use BLOCKED when decisive behavior, an artifact, or a required tool is unavailable.

This terminal report replaces the normal output. In verify, retain only domain evidence, state-model, selective reference, and verification rules needed for the declared criteria; skip candidate generation, pattern selection, composition, implementation, recommendations, routing, and handoffs.

Workflow

  1. Set scope and mode.

    • Use the resolved create, change, or review profile; verify follows the bounded contract above instead of this workflow.
    • Limit mixed requests to the interaction-pattern portion and name any necessary handoff.
    • Route away unresolved product discovery, pure visual direction, generic usability review, text editing, certification, and unrelated debugging.
    • If the primary request is a generic audit or asks for everything wrong without naming an interaction-pattern decision, stop after stating that boundary and the appropriate handoff. Do not manufacture a pattern decision from the artifact or produce audit findings.
  2. Inspect before proposing.

    • Read supplied artifacts and search the workspace for the existing implementation before asking for more context.
    • Require a concrete artifact for implementation or review: repository files, a screenshot, prototype, flow, recording, or sufficiently detailed markup/specification.
    • Locate established components, routes, state ownership, data loading, empty/error states, and relevant tests.
    • Distinguish observed facts, supported inferences, assumptions, and unknowns.
  3. Capture the decision context.

    • Record the task, actors, frequency, content scale, platform, input methods, state lifetime, latency, risk, and constraints only when they can change the choice.
    • Treat missing evidence as an assumption. Ask only when alternatives would produce materially different, costly, or irreversible work; otherwise choose a reversible default and flag it.
  4. Model the interaction before controls.

    • List the relevant objects, actions, states, relationships, transitions, entry, exit, failure, and recovery.
    • Identify the dominant screen purpose and navigation topology when applicable.
    • Remove unnecessary steps or unclear state boundaries before selecting a pattern.
  5. Load references selectively.

    • Use the routing rules below and read only the files needed for the current decision.
    • Do not load every reference or recite the reference set as a catalog.
  6. Compare viable candidates.

    • Generate no more than three candidates, including a direct unnamed solution when it may be simpler.
    • Compare user and task fit, content, state, navigation, feedback, recovery, input parity, platform, latency, and technical cost.
    • Reject mismatched candidates explicitly and choose one; do not return a neutral menu.
  7. Specify each retained pattern.

    • State: problem and context; observable fit signals; contraindications and failure modes; effects on content, state, navigation, and feedback; relevant variants; technical dependencies; observable verification criteria.
    • Write not applicable only with a reason. A pattern name without this record is incomplete.
  8. Compose the interaction.

    • Define screens or regions, entry and exit, actions, state ownership, transitions, and responsive/input variants.
    • Resolve conflicts between patterns, including Back, Close, Cancel, Done, focus, shortcuts, gestures, and shared regions.
    • Coordinate loading, empty, success, error, retry, cancel, undo, persistence, and synchronization.
    • Preserve selection, location, uncommitted work, and relevant history across switches unless a deliberate reset is safer.
  9. Act according to mode.

    • For create, deliver the decision and interaction blueprint.
    • For a read-only change, identify the current mismatch and the smallest coherent replacement without editing.
    • For an authorized change, reuse local conventions, edit the minimum coherent surface, and add or update proportionate tests.
    • For review, remain read-only and report evidence-backed mismatches and pass criteria. If the task asks to fix them, resolve the profile as change and require separate write authorization.
  10. Verify and report.

    • Exercise the critical task and alternate paths that affect safety, continuity, recovery, input, responsiveness, or latency.
    • Prefer observable interface behavior over static code inference when a runnable surface exists.
    • Report in proportion to the task; do not narrate this workflow.

Collaboration handoff

  • Accept an optional handoff with goal, evidence, constraints, decisions, open risks, artifact paths, and one next task; verify the artifacts instead of treating the handoff as proof.
  • Preserve facts, supported inferences, assumptions, unknowns, and prior decisions as distinct states. Reopen a prior decision only for new evidence or a blocking contradiction, and say why.
  • Own functional regions, relationships, state, behavior, semantic order, and responsive interaction changes; leave tracks, spans, gutters, and query geometry to structural layout work.
  • When structural layout is the next unresolved responsibility, produce a handoff of at most 400 words containing regions, invariants, DOM and focus order, states to preserve, responsive behaviors, constraints, risks, and artifact paths.
  • Record retained patterns and rejected alternatives in Decisions; keep unverified behavior and missing cases in Open Risks.
  • Set Next Task to one precise out-of-scope responsibility. Suggest an installed skill only when useful; otherwise name the capability. Do not require an upstream handoff or make later specialists mandatory.

Decision order

Prioritize:

  1. task completion, data integrity, and prevention of irreversible harm;
  2. fit to known users, evidence, frequency, skill, and context;
  3. state continuity, orientation, feedback, and recovery;
  4. accessible input parity and platform conventions;
  5. content scale, latency, and technical feasibility;
  6. consistency with the product and design system;
  7. learnability versus repeated-use efficiency;
  8. visual novelty.

When candidates are equivalent, choose the simpler, conventional, and reversible option. Change a structural mismatch instead of compensating with explanatory copy.

Reference routing

  • Read pattern-selection.md when requirements are sparse, multiple solutions are plausible, or a no-pattern option may win.
  • Read structure-and-navigation.md for screen purposes, workflow order, entry points, modes, hierarchy, parallel workspaces, orientation, or movement between places.
  • Read content-and-layout.md for functional hierarchy, disclosure, list/detail behavior, collections, pagination, or transformation across viewport sizes.
  • Read actions-and-state.md for command placement, asynchronous or destructive work, preview, progress, cancellation, undo, persistence, or cross-view continuity.
  • Read data-and-input.md for data exploration, filtering, structured or forgiving entry, autocomplete, defaults, validation, and recoverable form errors.
  • After the request passes the scope check, read verification.md for every in-scope review and after implementation; use it for scenario coverage, transition checks, navigation continuity, input parity, responsive behavior, latency, and evidence-backed findings.

If a task crosses domains, load only the intersecting references and keep ownership explicit. Let content/layout choose the spatial relationship; let actions/state define what survives transitions.

Output

Scale the response to the decision. For a small choice, provide context, decision, retained-pattern specification, and verification; keep each required field to a phrase when that is sufficient. For a workflow, revision, or implementation, use:

markdown
## Context
Facts, assumptions, task, platform, and constraints that affect the decision.

## Pattern decision
Up to three candidates, rejected alternatives, and the final choice.

## Pattern specification
The complete seven-field record for each retained pattern.

## Interaction blueprint
Objects, actions, regions, navigation, states, transitions, recovery, and variants.

## Implementation
Files/components changed or exact guidance. Include only when relevant.

## Verification
Scenarios exercised, observable pass criteria, tests, and residual risks.

Use tables only for comparisons or state mappings that become clearer in tabular form. Cite real artifact paths and line numbers when reviewing or implementing.

Quality rules

  • Link every choice to the concrete task and available evidence; never invent research, personas, or user behavior.
  • Resolve conflicting effects between selected patterns instead of listing them independently.
  • Specify state, feedback, recovery, and technical consequences wherever they can affect the task.
  • Verify current platform behavior, framework APIs, accessibility rules, and numeric requirements from authoritative current sources when they materially affect the decision.
  • Never claim accessibility certification from this workflow.
  • Preserve local product and code conventions unless they cause the interaction mismatch being fixed.
  • Run proportionate tests and state untested conditions or residual risks.
  • Keep implementation claims, review findings, and output length proportional to observed evidence and user impact.

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 Make It Flow AI skill do?

Select, compare, compose, specify, implement, and verify interaction patterns for screen-based interfaces. Use when choosing or revising navigation, screen structure, workflows, list/detail behavior, action and state models, forms, data exploration, feedback and recovery, responsive adaptations, or whether an implementation preserves interaction state, especially when alternatives have meaningful tradeoffs or behavior is unresolved. Do not use for product discovery, generic usability audits without a pattern decision, purely aesthetic polish, text-only editing, accessibility certification,...

Why use Make It Flow on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MrBlushu/blushu-design-skills/tree/main/plugins/blushu-design-skills/skills/make-it-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 Make It 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 Make It Flow?

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

Is the Make It Flow AI skill free?

Yes. It is published on GitHub by MrBlushu 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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