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Flowguard

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majiayu000
flowguard

Guard long, ambiguous, or stateful AI-agent work from drift. Use when the user asks to run or continue a multi-step task, autonomous loop, bug fix, repo change, PR readiness check, compaction handoff, resume from previous context, cost-control checkpoint, or any task likely to span many tool calls, files, sessions, agents, or verification gates.

Overview

Publishermajiayu000
Repositoryspellbook
Skill nameflowguard
Stars
280
Forks
26
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by majiayu000 on GitHub. Read the source before you install it.

Installation

Install the Flowguard 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.
  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/majiayu000/spellbook.git /tmp/spellbook
mkdir -p .claude/skills
cp -r /tmp/spellbook/skills/flowguard .claude/skills/flowguard
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Flowguard 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 Flowguard 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 Flowguard 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.

Flowguard

Overview

Use this skill as the single lifecycle entrypoint for agent work that can drift, lose context, or become expensive. It routes the task, verifies current state, runs bounded execution loops, and leaves a resumable handoff.

This skill coordinates other skills; it does not replace them. Use task-specific skills such as systematic-debugging, comprehensive-testing, codex-retrospective, or vibeguard only when their trigger is clearly met.

Operating Contract

  • Do not start a long autonomous loop until route, scope, and stop conditions are explicit.
  • Do not treat memory, summaries, or handoffs as current truth until repo, git, files, runtime, or remote state is verified.
  • Do not claim completion without fresh verification evidence from the current session.
  • Do not expand scope, touch destructive surfaces, or cross file-ownership lanes without stopping to re-route.
  • Re-state the primary objective and a plan of no more than five steps before major phase changes.
  • Prefer one controlling checkpoint over many specialized workflow fragments when the task risk is context loss, drift, or compounding errors.
  • Before commit, push, PR, merge, or applying agent-generated changes outside the already-approved scope, call review-gate or produce the same review pack and wait for explicit human approval.

Route First

Choose one route before editing files or running a long loop:

RouteUse WhenAction
execute_directGoal, context, constraints, and done-when are clear; scope is small or verification is cheap.Work directly with short checkpoints.
plan_firstWork spans many files, sessions, agents, architecture decisions, migrations, or risky sequencing.Create a brief execution plan or use the relevant planning skill before edits.
clarify_firstGoal, target files, constraints, done-when, destructive permission, production impact, or ownership is unclear.Ask the smallest blocking question before continuing.

Do not hide ambiguity inside assumptions. If a wrong assumption would cause large rewrites, production risk, data loss, credential exposure, or wasted long-loop cost, use clarify_first.

Startup

  1. Search first for existing files, skills, plans, or prior artifacts that may already cover the task.
  2. Load every applicable AGENTS.md for files that may be edited.
  3. Run the state snapshot when working in a repo or resuming. Resolve it from the installed Flowguard skill directory, not from the target repo:
bash
# From the installed flowguard skill directory, the directory containing this SKILL.md:
scripts/workflow_state_snapshot.sh /path/to/target/repo

When already in the target repo, pass . as the target to the installed script, for example /path/to/installed/flowguard/scripts/workflow_state_snapshot.sh ..

  1. If the task continues previous work, treat memory and summaries as hints only. Verify cwd, git branch, dirty files, relevant artifacts, and runtime state before relying on them.
  2. Capture the four task elements: goal, context, constraints, and done-when. If one is missing and risky, clarify.

Preflight Contract

Before substantial work, write or state the compact preflight:

text
route:
goal:
context:
constraints:
done_when:
out_of_scope:
verification_commands:
stop_conditions:
handoff_location:
objective_restatement:
plan_5_steps_or_less:

For short direct tasks, this can be one concise paragraph. For long tasks, make it explicit and keep it available for compaction or resume.

Execution Loop

Use a step-test-update loop:

  1. Select one current step with owned files and an expected check.
  2. Re-state how the step supports the primary objective.
  3. Announce the edit boundary before changing files.
  4. Make the smallest useful change.
  5. Run focused verification for that step when feasible.
  6. Record a checkpoint with changed files, command results, decisions, blockers, context audit, and next step.

Stop and re-evaluate when any condition occurs:

  • The same fix fails 3 times.
  • Scope expands beyond the preflight.
  • Required data is missing or stale.
  • A tool result conflicts with the plan.
  • Tests or builds fail for reasons unrelated to the current hypothesis.
  • The user sends a newer instruction that changes priority.
  • Token, tool-call, wall-time, or external-cost budget is exceeded.

Failure Modes

  • Assumption drift: the route says execute_direct, but new evidence shows missing goal, constraints, or done-when. Stop and re-route.
  • Summary-of-summary loss: compaction or handoff omits modified files, decisions, or verification commands. Rebuild state from local truth before editing.
  • Stale memory: remembered project facts conflict with current files, git, runtime, or GitHub state. Use current evidence.
  • Silent tool failure: an empty, partial, or "close enough" tool result becomes input for later steps. Mark it as a blocker or rerun with a narrower check.
  • Parallel merge risk: two lanes need the same writable file. Collapse to one integration owner before continuing.

Verification Gate

Do not claim completion from expectation or older output. Report fresh evidence from this session.

Pick checks from the repo, AGENTS.md, and changed surface. Common defaults:

StackBefore CompletionBefore Submission
Rustcargo checkcargo test
TypeScriptnpx tsc --noEmitproject test command
Gogo build ./...go test ./...
Pythonfocused import/type/lint check if presentpytest

If a check cannot run, say why and name the nearest useful fallback that did run.

Review Gate

Before landing agent-generated changes, produce a concise review pack or use the review-gate skill. The pack must include intent, diff summary, changed files, risks, verification, open questions, and the exact action needing approval. Human approval for one action does not automatically authorize a different action such as merge.

Handoff And Resume

Read references/state-contract.md when asked to create a handoff, resume after compaction, continue a previous task, or prepare automation.

Required handoff fields:

  • modified files
  • constraint set or SPEC
  • verification command and result
  • key decisions
  • current priority
  • L1-L7 rule summary when VibeGuard applies
  • context audit: keep, externalize, discard, and stale/conflicting inputs
  • review gate decision when changes are ready to land
  • blockers and next action

Resume must start by comparing the handoff with current local truth. If cwd, branch, files, tests, or user priority changed, update the plan before editing.

Multi-Agent Rule

Use parallel agents only when file ownership is disjoint and merge ownership is explicit. A delegation must name:

  • agent or lane
  • writable files or directories
  • read-only context
  • expected output artifact
  • verification owner
  • merge owner
  • stop conditions

If two agents need to write the same file, do not run them in parallel.

Automation Boundary

Skill workflows are manual first. Automate only after the workflow has been manually validated on real tasks. Scheduled automation should start as read-only: state scans, handoff drafts, stale-worktree reports, or verification summaries. Code edits, deploys, credential changes, or PR submissions require explicit user intent unless a separate trusted automation contract exists.

Resources

  • scripts/workflow_state_snapshot.sh <path>: read-only snapshot for cwd, git state, nearby agent instructions, dirty files, and likely verification commands.
  • references/state-contract.md: templates for preflight, checkpoints, handoff, resume, loop guards, and automation readiness.
  • review-gate: review pack and explicit human approval before landing agent-generated diffs.

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 Flowguard AI skill do?

Guard long, ambiguous, or stateful AI-agent work from drift. Use when the user asks to run or continue a multi-step task, autonomous loop, bug fix, repo change, PR readiness check, compaction handoff, resume from previous context, cost-control checkpoint, or any task likely to span many tool calls, files, sessions, agents, or verification gates.

Why use Flowguard on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/majiayu000/spellbook/tree/main/skills/flowguard. 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 Flowguard?

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 Flowguard?

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

Is the Flowguard AI skill free?

Yes. It is published on GitHub by majiayu000 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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