Workflow logo

Workflow

Community
notque
workflow

Structured work: multi-phase tasks, feature builds, planning, objective loops, hill climbing.

Overview

Publishernotque
Repositoryvexjoy-agent
Skill nameworkflow
Stars
443
Forks
50
Bundled files
136
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.

  • 136 bundled files

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

  • Open source

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

Installation

Install the Workflow 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/notque/vexjoy-agent/tree/main/skills/process/workflow
  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/notque/vexjoy-agent.git /tmp/vexjoy-agent
mkdir -p .claude/skills
cp -r /tmp/vexjoy-agent/skills/process/workflow .claude/skills/workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Workflow 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 Workflow 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 Workflow 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.

Workflow Skill

Five modes for structured multi-phase work. Match the request, follow that mode's instructions.

Mode Selection

Request patternMode
Feature design/plan/implement/validate/release, end-to-endFeature Lifecycle
Write spec, define requirements, create plan, interview, pause/resumePlanning
Keep working until, iterate until done, drive to doneObjective Loop
Make faster, speed up, reduce latency, profile, hill climbHill Climb
All other structured workflows: review, debug, refactor, research, create, explore, upgradeAd-Hoc Workflow

Feature Lifecycle

Phase-gated workflow: DESIGN > PLAN > IMPLEMENT > VALIDATE > RELEASE. Each phase must pass its gate before the next begins.

Phase Routing

If .feature/ exists, check state: python3 ~/.claude/scripts/feature-state.py status. Route to the indicated phase.

If no feature state exists, determine entry from intent:

  • "design", "think through", "explore approaches" -> DESIGN
  • "plan", "break down", "create tasks" -> PLAN (requires completed design)
  • "implement", "execute plan" -> IMPLEMENT (requires completed plan)
  • "validate", "quality gates" -> VALIDATE (requires completed implementation)
  • "release", "merge", "ship it" -> RELEASE (requires passed validation)
  • "end to end", "full lifecycle" -> DESIGN (start from beginning)

Phase References

Load the phase reference, then follow it exactly:

PhaseReferenceProduces
DESIGNreferences/fl-design.mddesign.md
PLANreferences/fl-plan.mdWave-ordered task list
IMPLEMENTreferences/fl-implement.mdCode changes
VALIDATEreferences/fl-validate.mdQuality gate report
RELEASEreferences/fl-release.mdMerged PR
End-to-endreferences/fl-pipeline.mdFull lifecycle
State conventionsreferences/fl-shared.md--
Error recoveryreferences/fl-error-handling.md--

State operations use python3 ~/.claude/scripts/feature-state.py only. Never manipulate state files directly.


Planning

Spec writing, plan creation, interviews, ambiguity triage, and session pause/resume. Planning owns specs and saved plans; execution goes through subagent-driven-development or workflow dispatch.

Sub-mode Routing

SignalReference
Write spec, user stories, define requirements, scope, acceptance criteriareferences/pl-spec.md
Discuss ambiguities, resolve gray areas, pre-planning discussionreferences/pl-pre-plan.md
Interview me, depth-first review, "not sure", "where do I start", "poke holes"references/pl-depth-first-interview.md
Implicit ambiguity or unclear implementation choicesreferences/pl-ambiguity-triage.md
Another person holds needed facts or approvalreferences/pl-human-source-elicitation.md
Observation can settle a disputed choicereferences/pl-empirical-prototype.md
Phase or session transition nearreferences/pl-context-boundary.md
Create plan, task plan, file-backed planningreferences/pl-plan-files.md
Check plan, validate plan, pre-execution checkreferences/pl-check.md
List plans, show plan, complete plan, manage plansreferences/pl-manage.md
Pause, save progress, handoff, stopping for nowreferences/pl-pause.md
Resume, continue, pick up where I left offreferences/pl-resume.md

For interviews, batch independent questions into frontier rounds. Ask dependent questions sequentially. Include a recommendation per question.


Objective Loop

Iterate-until-verified-done loop. A user states an objective with verifiable done-criteria; each iteration routes one /do cycle, verifies by executing the criteria, and reschedules until verified-done or budget-stop.

Phase 1: SPEC

Gather from the request: objective statement, DONE-CRITERIA (verifiable checks), iteration budget (default 5), NOT-DONE-YET guardrails (what may never be done to satisfy a criterion).

DONE-CRITERIA types: command (preferred -- deterministic command with expected exit code/output) or rubric (only when no mechanical check exists -- frozen at SPEC time, graded by a fresh-context agent).

Phase 2: STATE

Write .objective/<slug>/state.md from references/ol-state-file.md. Wakeups resume from the state file, never conversation memory.

Phase 3: ITERATE

Plan the smallest next step. Route through /do: classify -> route -> dispatch agents -> evaluate. The loop dispatches exclusively through /do -- never edit inline.

Phase 4: VERIFY

Run every done-criterion check. A worker's "passes" claim never substitutes for re-running.

  • command: run it, paste exit code and output into iteration log.
  • rubric: dispatch fresh-context sub-agent (did NOT produce the work) with artifact + rubric only. Returns PASS/FAIL with cited evidence.

All pass -> final report, STOP. Any unmet -> Phase 5.

Criteria-gaming guard: a criterion may never be satisfied by weakening a hook, gate, test, or safety control. Stop and report the conflict if that is the only visible path.

Phase 5: RESCHEDULE or STOP

All pass: stop. Unmet + iterations remain: update state file, call ScheduleWakeup (delay 270s for active polling, 1200s+ for idle work). Budget exhausted: honest NOT-DONE report with per-criterion status.


Hill Climb

Metric-driven optimization loop. One number moves; everything else stays fixed. Each iteration: hypothesis -> one change -> correctness floor -> re-measure -> accept or revert.

Phase 1: SPEC

FieldRequiredDefault
METRIC (one number, units, direction)yes--
MEASURE (deterministic command)yes--
TARGET (value that ends the loop)yes--
FLOOR (correctness gate commands, must exit 0)yes--
FIXTURE (pinned dataset/workload)yes--
Variance toleranceno2x baseline spread
Iteration budgetno8
Plateau threshold Kno3

One METRIC per loop. Two numbers with a trade-off: promote one to the FLOOR. Load references/hc-domain-playbooks.md for pre-filled SPEC blocks per domain (frame rate, API latency, CI time, bundle size, memory, token cost).

Phase 2: BASELINE

Run MEASURE N times (N >= 5, N >= 10 for wall-clock). Record median and spread. If spread >= target improvement: STOP -- harness too noisy. Report noise sources and offer to stabilize first.

Phase 3: PROFILE

Locate the cost before changing anything. Load references/hc-profiling-tools.md for per-domain tooling. Guessing at hot spots is the dominant failure mode.

Phase 4: HYPOTHESIZE and CHANGE

State one hypothesis targeting the profiled hot spot. Make one change. Run FLOOR commands -- revert immediately if any fail.

Phase 5: MEASURE

Run MEASURE N times. Compare median to baseline. Accept only if delta > variance tolerance. Update ledger (references/hc-ledger.md). If accepted, new baseline.

Phase 6: LOOP or STOP

Target reached: final report. K consecutive non-improving iterations: plateau stop. Budget exhausted: report what worked and what remains.


Ad-Hoc Workflow

For structured multi-phase work that does not fit the four modes above. Identify the workflow from the table, load its reference, follow its phases exactly.

Cost Gate

Ask first: does this need a multi-agent workflow? Skip the workflow when: single-file mechanical edit (use quick), one agent satisfies the request (direct dispatch), lookup/status/count (direct agent). Escalate only when the request has independent subtasks, needs orthogonal verification, or names "comprehensive / thorough / adversarial / tournament."

Composable Patterns

PatternWhat it does
Classify-and-actRoute by type up front; or classify-at-end
Fan-out-and-synthesizeIndependent agents in parallel, barrier, one synthesizer
Adversarial verificationExecutor builds, fresh skeptic refutes
Generate-and-filterOver-generate candidates, gate keeps survivors
TournamentN agents attempt same task; pairwise judges pick winner per round
Loop-until-doneRepeat until hard completion test passes
QuarantineRead-only triage agent for untrusted content; separate privileged acting agent

Workflow Catalog

Load the reference for the matched workflow. references/... paths resolve under ${CLAUDE_SKILL_DIR}.

CategoryWorkflowReference
Code ReviewComprehensive multi-wavereferences/comprehensive-review.md
DebuggingEvidence-based diagnosisreferences/systematic-debugging.md
RefactoringSafe refactoring with test gatesreferences/systematic-refactoring.md
ResearchFormal research with source gatesreferences/research-pipeline.md
ResearchResearch to articlereferences/research-to-article.md
ContentArticle evaluationreferences/article-evaluation-pipeline.md
ContentDe-AI contentreferences/de-ai-pipeline.md
ContentDocumentationreferences/doc-pipeline.md
ExplorationCodebase explorationreferences/explore-pipeline.md
ExplorationMulti-perspective analysisreferences/do-perspectives.md
CreationSkill creationreferences/skill-creation-pipeline.md
CreationHook developmentreferences/hook-development-pipeline.md
CreationMCP serverreferences/mcp-pipeline-builder.md
CreationPipeline scaffoldingreferences/pipeline-scaffolder.md
CreationDomain researchreferences/domain-research.md
CreationChain compositionreferences/chain-composer.md
CreationAuto-pipeline generationreferences/auto-pipeline.md
UpgradeAgent/skill upgradereferences/agent-upgrade.md
UpgradeSystem upgradereferences/system-upgrade.md
UpgradeToolkit improvementreferences/toolkit-improvement.md
TestingPipeline test runnerreferences/pipeline-test-runner.md
TestingPipeline retroreferences/pipeline-retro.md
GitHubProfile rules extractionreferences/github-profile-rules.md
GitHubFix PRs in repos you don't ownreferences/pr-fix-loop.md
OrchestrationTask orchestrationreferences/workflow-orchestrator.md
OrchestrationDAG compositionreferences/dag-composition-patterns.md
OrchestrationCompatibility matrixreferences/dag-compatibility-matrix.md
OrchestrationCommon DAG patternsreferences/dag-skill-patterns.md
OrchestrationDAG examplesreferences/dag-orchestration-examples.md
OrchestrationDAG advancedreferences/dag-orchestration-advanced.md
OrchestrationFeedback loopreferences/feedback-loop-construction.md

Terminology

"Workflow" is the canonical term. "Pipeline" is the retained legacy alias -- kept for back-compat in routing keys, meta.name exports, and pipeline-index.json. Use "workflow" in new prose; do not rename code identifiers.


Error Handling

ErrorResponse
Mode ambiguousAsk the user to clarify intent
Phase mismatchReport current state, suggest correct next phase
Missing artifactRoute back to previous phase
Noisy harness (hill-climb)Stop; report spread vs target improvement
Budget exhaustedHonest NOT-DONE report with per-criterion status
State file missing on wakeupReport and stop; ask user to restate objective

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 76 more files.

Frequently asked questions

What does the Workflow AI skill do?

Structured work: multi-phase tasks, feature builds, planning, objective loops, hill climbing.

Why use Workflow on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/notque/vexjoy-agent/tree/main/skills/process/workflow. 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 Workflow?

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 Workflow?

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

Is the Workflow AI skill free?

Yes. It is published on GitHub by notque 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇