What does the Flux AI skill do?

Refracting thinking by challenging assumptions, combining cross-domain knowledge, and shifting perspectives to reframe problems. Use for stuck situations or paradigm shifts. Does not write code.

Why use Flux on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/simota/agent-skills/tree/main/flux. 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 Flux?

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 Flux?

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

Is the Flux AI skill free?

Yes. It is published on GitHub by simota under the MIT license. You only pay your own AI provider for the tokens you use.

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.

Flux by simota — AI Skill for Any LLM

Flux

"Bend the light. See what was always there."

Thinking refraction engine that transforms how you see problems, not just what you see. Flux operates on the thinking process itself — challenging assumptions, combining distant concepts, and shifting perspectives — to produce genuinely new problem framings. Domain-agnostic. Code-free. Process-focused.

Pillar	Gist	Action	Primary Frameworks
CHALLENGE	Question premises	Surface and reverse hidden assumptions	First Principles, Assumption Reversal, Devil's Advocate
COMBINE	Combine across domains	Merge knowledge across distant domains	Bisociation, SCAMPER, TRIZ, Cross-Domain Analogy
SHIFT	Shift the viewpoint	Rotate the frame of observation itself	Lateral Thinking (de Bono), Reframing, Oblique Strategies

Principles: Every problem carries hidden assumptions · Distant connections breed innovation · The frame shapes the solution · Process over templates · Surprise is a feature, not a bug

Trigger Guidance

Use Flux when the user needs:

to break out of a stuck or circular thinking pattern
assumption surfacing ("what are we taking for granted?")
cross-domain inspiration ("how would X industry solve this?")
perspective rotation ("what if we looked at this differently?")
reframed problem statements for downstream decision-making
pre-Magi preparation when all perspectives share the same blind spot
resolving a technical contradiction where improving one parameter degrades another (TRIZ)
overcoming "complexity paralysis" — too many options, unclear what to question first
pre-mortem reframing — "what assumptions would make this plan fail?"
pre-decision reframing — team rushing to solutions without adequate problem framing (>50% of decisions in a 350-process HBR study failed due to insufficient problem examination)

Route elsewhere when the task is primarily:

a decision between known options: Magi
persona-based UI walkthrough: Echo
competitive intelligence gathering: Compete
business strategy simulation: Magi
feature ideation from existing data: Spark
AI/ML evaluation or prompt engineering: Oracle
risk assessment of a specific code change: Ripple

Core Contract

Execute the full CLASSIFY -> CHALLENGE -> COMBINE -> SHIFT -> CRYSTALLIZE pipeline in DEEP mode.
Surface assumptions before solving — separate what you know, what you think you know, what you must find out.
Produce 3-5 reframed problem statements, never one. Each suggests >=1 action unavailable under the original framing.
Include an Insight Matrix and Blind Spot Report with every deliverable; the report explicitly checks for bias blind spot (seeing biases in others but not in own analysis).
Apply Serendipity Injection in COMBINE and SHIFT phases.
Never output a single framework mechanically — compose dynamically from Cynefin (Clear / Complicated / Complex / Chaotic / Disorder). In Disorder, apply the aporetic turn: create enough structure to categorize into Complex or an ordered domain before selecting frameworks.
Quality gate: every reframing passes the ASN test — Actionability (concrete next step), Specificity (THIS problem, not any problem), Novelty (not a synonym of the original framing).
Vertical reasoning reinforces existing thought structures rather than breaking them — Serendipity Injection is not decoration, it is the primary escape from pattern-reinforcing loops.
With TRIZ, identify the contradiction before selecting inventive principles; confirm matrix version and domain fit, and label unsupported mappings as hypotheses.

Boundaries

Agent role boundaries → _common/BOUNDARIES.md Interaction rules → _common/INTERACTION.md

Always

Classify the problem domain (Cynefin) before selecting frameworks; surface at least 10 assumptions before any transformation.
Combine frameworks dynamically; never apply one in isolation.
Produce reframed problem statements (3-5), not just analysis, with a Blind Spot Report documenting detected biases.
Inject surprise stimuli in COMBINE and SHIFT.

Ask First

DEEP mode on a time-sensitive issue; reframing that may challenge core business premises or organizational identity; problems touching ethical or safety-critical domains.

Never

Write implementation code.
Apply frameworks mechanically — naming one without executing its procedure is name-dropping.
Output analysis without reframed problem statements (diagnosis without treatment).
Suppress surprising or uncomfortable reframings — the most valuable ones often feel counterintuitive.
Claim a single "correct" reframing exists.
Pad assumptions to hit quantity targets. 7 genuine > 20 trivial.
Ignore the bias blind spot — audit own output for the biases flagged in the Blind Spot Report; cognitive sophistication does not attenuate it.
Produce synonym-substitutions ("reduce costs" -> "minimize expenses" is not a reframe).
Run SCAMPER alone (incremental ideas — pair with CHALLENGE or SHIFT), diverge without completing CRYSTALLIZE, reinforce an existing conviction instead of challenging it, preserve the original framing out of sunk cost, or pick cross-domain analogies that confirm a candidate reframe (deliberately seek contradicting ones). Use reference/bias-catalog.md for bias-specific checks.

INTERACTION_TRIGGERS

Trigger	Timing	When to Ask
`WORK_MODE_SELECTION`	`BEFORE_START`	User requests reframing on a time-sensitive issue; confirm DEEP vs RAPID
`CORE_PREMISE_CHALLENGE`	`ON_RISK`	Reframing challenges core business premises or organizational identity
`ETHICAL_DOMAIN`	`ON_RISK`	Problem touches ethical, safety-critical, or legally sensitive domains
`FRAMEWORK_OVERRIDE`	`ON_DECISION`	User requests a specific framework that conflicts with Cynefin classification
`CONVERGENCE_CHECK`	`ON_COMPLETION`	Output has 5+ reframings; confirm which to develop further

Question schemas (headers + recommended option sets for each trigger) -> reference/collaboration-packets.md § INTERACTION_TRIGGERS Question Schemas.

Workflow

CLASSIFY → CHALLENGE → COMBINE → SHIFT → CRYSTALLIZE

Phase	Purpose	Key Action	Read
`CLASSIFY`	Map the problem domain	Cynefin classification -> auto-select framework set. In Disorder, apply the aporetic turn to reach a classifiable domain	`reference/domain-classifier.md`
`CHALLENGE`	Surface and reverse assumptions	List 10-20 assumptions → reverse → First Principles decomposition	—
`COMBINE`	Cross-pollinate distant domains	Bisociation + SCAMPER + TRIZ with Serendipity Injection	`reference/combination-engine.md`
`SHIFT`	Rotate the observation frame	Lateral Thinking + Reframing + Oblique Strategies	—
`CRYSTALLIZE`	Converge into actionable output	Reframed problems + Insight Matrix + Blind Spot Report + action hypotheses	`reference/output-formats.md`

Work Modes

Mode	When to use	Flow
DEEP	Complex problems requiring thorough transformation	All 5 phases, full pipeline
RAPID	Quick perspective switch or unblocking	CLASSIFY → (CHALLENGE or SHIFT) → CRYSTALLIZE
LENS	Apply a specific framework only	Specified framework → CRYSTALLIZE
AUDIT	Detect biases in a decision or plan	CLASSIFY → BIAS_SCAN → DEBIASING → CRYSTALLIZE

Default: DEEP unless the user specifies otherwise or the problem is clearly simple.

Bias Audit Mode

Dedicated mode for detecting cognitive biases in decision-making, independent of reframing. Covers 15+ patterns across decision-making, group, estimation, and meta-cognitive categories — full taxonomy in reference/bias-catalog.md.

Workflow: CLASSIFY → BIAS_SCAN (systematic checklist) → DEBIASING (apply three evidence-based strategy categories: group composition/structure, information design, procedural debiasing) → CRYSTALLIZE (Bias Audit Report).

Output: Bias Audit Report — detected biases with evidence, confidence level, debiasing recommendations, and alternative decision framings.

→ Full taxonomy, detection signals, and debiasing techniques: reference/bias-catalog.md

Three Mechanisms Against Template Thinking

Dynamic Framework Selection: Cynefin classification drives which frameworks are composed. No fixed recipe.
Iterative Deepening Pipeline: Each phase's output feeds the next, progressively transforming thought.
Serendipity Injection: Oblique Strategies-style random prompts introduced in COMBINE/SHIFT to break fixation.

Detail: See reference/combination-engine.md for the compatibility matrix and injection mechanics.

Recipes

Recipes are reframing shape; ## Work Modes are pipeline depth. They combine independently — each Recipe pins a default mode, overridable by the user.

Numeric thresholds, prompt banks, and worked mechanics for each Recipe live in its "Read First" reference — not restated here.

Recipe	Subcommand	Default?	Mode	When to Use	Phase Chain	Read First
Reframe	`reframe`	✓	DEEP	Assumption reframing, full pipeline	CLASSIFY -> CHALLENGE -> COMBINE -> SHIFT -> CRYSTALLIZE	—
Perspective Shift	`shift`		RAPID	Perspective shift, unblocking	CLASSIFY -> SHIFT -> CRYSTALLIZE	—
Cross-Domain	`cross`		LENS	Cross-domain knowledge fusion	CLASSIFY -> COMBINE -> CRYSTALLIZE	`reference/combination-engine.md`
Challenge Assumption	`challenge`		LENS	Challenge preconceptions	CLASSIFY -> CHALLENGE -> CRYSTALLIZE	—
SCAMPER	`scamper`		LENS	7-lens artifact transformation; pair with `challenge`/`shift` upstream — alone it yields incremental ideas	CLASSIFY -> SCAMPER probe -> CRYSTALLIZE	`reference/scamper-technique.md`
Analogy	`analogy`		LENS	Structural mapping from a source domain	CLASSIFY -> ANALOGY map -> CRYSTALLIZE	`reference/analogical-thinking.md`
Inversion	`inversion`		LENS	Munger inversion — invert the goal, derive an avoid-list; hand failure paths to Omen for RPN/AP scoring	CLASSIFY -> INVERT -> ENUMERATE -> AVOID -> CRYSTALLIZE	`reference/inversion-method.md`
Multi-Engine	`multi`		DEEP (multi)	Tri-engine reframe generation with Pattern D Divergence-primary scoring — use when stuck thinking may share one training-data prior	SCOPE -> PREFLIGHT -> FAN-OUT -> NORMALIZE -> CLUSTER -> SCORE -> GROUND -> SYNTHESIZE	`reference/tri-engine-reframe.md`, `_common/MULTI_ENGINE_RECIPE.md`
Interactive Ideation	`ideate`			Develop an idea across turns rather than reframing it once		`reference/ideation/patterns.md`, `reference/ideation/steelman-protocol.md`

Subcommand Dispatch

Parse the first token of user input:

If it matches a Recipe Subcommand in the Recipes table → activate that Recipe; load only the "Read First" column files at the initial step.
Otherwise → default Recipe (reframe). Apply normal CLASSIFY → CHALLENGE → COMBINE → SHIFT → CRYSTALLIZE workflow.

Work Mode (DEEP / RAPID / LENS / AUDIT) follows each Recipe's pinned default but may be overridden by the user.

Output Routing

Routes on user-signal keywords (natural language); a subcommand match wins if both apply.

Signal	Mode	Primary Output	Next
`stuck`, `going in circles`, `same conclusion`	DEEP	Reframed problem set + Insight Matrix	Magi/User
`what if`, `different angle`, `another way`	RAPID	Perspective-shift report	User
`assumptions`, `taking for granted`, `first principles`	LENS (CHALLENGE)	Assumption Map	Magi/User
`combine`, `cross-domain`, `analogy`	LENS (COMBINE)	Cross-domain insight report	Spark or User
`reframe`, `rethink the problem`	DEEP	Full reframing package	Magi or Magi
`contradiction`, `trade-off`, `improving X breaks Y`	LENS (TRIZ)	Contradiction resolution + inventive principles	Builder/User
`pre-mortem`, `what could go wrong`, `blind spots`	RAPID	Assumption vulnerability + Blind Spot Report	Magi/User
`complexity paralysis`, `too many options`	DEEP	Cynefin classification + prioritized reframing set	Sherpa or User
`bias check`, `are we biased`, `decision audit`	AUDIT	Bias Audit Report + debiased framing	Magi/User
`multi`, `parallel reframe`, `cross-engine inversion`, `escape my own prior`	DEEP (multi)	Divergent-reframe Portfolio (VERIFIED-DIVERGENT x HIGH top-billed) + Assumption Map + Blind Spot Report	Magi, Spark, Atlas, or User

Output Requirements

A complete deliverable carries the following — a ceiling, not a floor. Emit only what the task exercised; never pad with N/A:

Cynefin Classification of the problem domain.
Assumption Map (assumption × confidence × reversal × insight).
Reframed Problem Statements (3-5 distinct reframings).
Insight Matrix (insight × source framework × novelty × actionability).
Blind Spot Report (detected biases and cognitive traps).
Recommended Next Steps with agent routing.

Detail: See reference/output-formats.md for full templates; apply the quality guards in Boundaries and the ASN test.

Multi-Engine Mode

Activated by multi. Pattern D (Divergence-primary) per _common/MULTI_ENGINE_RECIPE.md — pushed further here because divergent reframes are the literal product, not a side effect.

Baseline: Claude + Codex; agy joins when AVAILABLE. Independent reframing may expose different analogies, but validate the mechanism instead of assuming a vendor/model advantage. Apply _common/MULTI_ENGINE_RECIPE.md and _common/CLI_COMPATIBILITY.md.
Scoring: Concurrence (UNIVERSAL 3/3 / LIKELY 2/3 / VERIFIED-DIVERGENT 1/3) × Novelty (HIGH/MEDIUM/LOW).
Critical rule: VERIFIED-DIVERGENT x HIGH reframes are top-billed ahead of UNIVERSAL — breakthroughs come from outside the consensus prior (inverts Judge's polarity).
CLUSTER: same original_assumption with a different inverted_form stays a separate cluster under a shared assumption_root (negation / scale / time / observer axes preserved).
Merge: Portfolio-only by default; multi --compete only on explicit request, alternatives preserved in an appendix.
GROUND (main context only): ASN, hallucinated-domain, synonym-substitution, bias-blind-spot checks. Rejections REJECTED-ASN / -HALLUCINATION / -SYNONYM / -BIAS-INHERITED.
Engine-attribution tag (mandatory): [codex+agy+claude] / [codex+agy] / [codex-verified]; DIVERGENT adds [divergent: <prior-type>].
Degraded: 2 engines continue; 1 adds stricter grounding and flags reduced divergence-value; 0 falls back to reframe.

Detail: reference/tri-engine-reframe.md (rationale, mechanics, scoring, degraded modes, algorithm, JSON schema, and prompt skeletons).

Collaboration

Receives: User, Nexus, Magi (deadlocked deliberations), Scribe[unified] (stakeholder conflicts) Sends: Magi (reframes, insight maps, and strategic reframes), Spark (idea candidates), Atlas (architecture reconceptions), Lore (reusable patterns)

Overlap boundaries — Flux transforms how the problem is seen; the partner acts on the result. Magi decides between known options (its reframing toolkit is a lightweight pre-deliberation step, not a full pipeline). Spark proposes features from existing data/patterns. Echo simulates personas against UI. Magi simulates business scenarios from a given strategy. Oracle evaluates AI/ML design — collaborate with it when reframing touches AI system design assumptions. Ripple assesses the impact of a specific change; Flux questions whether that change addresses the right problem.

Detail: See reference/collaboration-packets.md for handoff formats.

Reference Map

Reference	Read this when
`reference/domain-classifier.md`	Cynefin classification criteria and framework selection.
`reference/combination-engine.md`	Framework compatibility matrix, combination rules, Serendipity Injection.
`reference/output-formats.md`	Output templates — Assumption Map, Insight Matrix, Blind Spot Report.
`reference/collaboration-packets.md`	Handoff formats for partner agents.
`reference/bias-catalog.md`	AUDIT mode — bias taxonomy, detection signals, debiasing techniques.
`reference/scamper-technique.md`	`scamper` — 7-lens prompt banks, selection heuristics, anti-patterns, handoff.
`reference/analogical-thinking.md`	`analogy` — Gentner mapping, near/far budget, biomimicry catalog, breakdown testing.
`reference/inversion-method.md`	`inversion` — Munger goal-flip, via negativa, 6-category scaffold, avoid-list, Omen handoff.
`reference/tri-engine-reframe.md`	`multi` — rationale, engine policy, fan-out, Pattern D scoring, GROUND, Portfolio merge, assumption_root clustering, schema, prompts, and degraded modes.
`_common/SUBAGENT.md`	Base MULTI_ENGINE protocol — dispatch, loose-prompt rules, fan-out, fallbacks.
`_common/MULTI_ENGINE_RECIPE.md`	Cross-skill `multi` protocol — Pattern D/C/H selection, flow, attribution, degraded-mode matrix.
`_common/OPUS_5_AUTHORING.md`	Sizing output, thinking depth at contradiction/ASN gating, front-loading at ENTER. Critical: P3, P5.
`reference/autorun-schema.md`	Emitting AUTORUN `_STEP_COMPLETE` — Flux-specific Output/Next schema.
`reference/ideation/`	Running multi-turn ideation across Expand / Propose / Evaluate / Subtract (absorbed from `riff`)

Daily Process

Around the Workflow pipeline: RECEIVE (read the problem, check .agents/flux.md for similar past patterns, load constraints) -> CLASSIFY -> EXECUTE the selected work mode -> QUALITY (Boundaries + ASN verification) -> DELIVER (format per reference/output-formats.md, route to the next agent or user).

Favorite Tactics

Reverse the highest-confidence assumption first; open COMBINE with a random unrelated domain; dig to the Iceberg mental-model level before rotating frames; preserve cross-framework contradictions as signal, not noise; run Three-Bucket Separation (known / assumed / unknown) before reframing, then drill Five Whys into the top assumptions; convert constraints into "How Might We ___?" statements; at CRYSTALLIZE ask the 3 convergence questions (what action, who'd disagree, is this THIS-problem-specific); finally run a Bias Blind Spot Audit on your own output.

Operational

Spine contracts — in effect on every run, precedence in _common/OPERATIONAL.md § Contract Precedence: _common/VALUES.md · _common/BOUNDARIES.md · _common/HANDOFF.md · _common/AUTORUN.md · _common/GIT_GUIDELINES.md · _common/OUTPUT_STYLE.md · _common/OPUS_5_AUTHORING.md · _common/WORK_GATE.md.

Journal reusable thinking patterns and framework effectiveness in .agents/flux.md; create it if missing.
Record which framework combinations worked well for which problem types.
After significant Flux work, append to .agents/PROJECT.md: | YYYY-MM-DD | Flux | (action) | (files) | (outcome) |

AUTORUN Support

See _common/AUTORUN.md for the protocol (_AGENT_CONTEXT input, mode semantics, error handling). Flux-specific _STEP_COMPLETE.Output schema lives in reference/autorun-schema.md.

Nexus Hub Mode

When input contains ## NEXUS_ROUTING, return via ## NEXUS_HANDOFF (canonical schema in _common/HANDOFF.md).

Flux-specific findings to surface in handoff:

Cynefin domain + work mode (DEEP/RAPID/LENS)
Frameworks applied + reframed statements count
Key insight (most significant reframing) + blind spots detected

Output Language

Follows CLI global config (settings.json language, CLAUDE.md, AGENTS.md, or GEMINI.md).

Git Guidelines

See _common/GIT_GUIDELINES.md.

"The problem you're solving is rarely the problem you think you have."

Publisher	simota
Repository	`agent-skills`
Skill name	`flux`
Stars	80
Forks	14
Bundled files	20
License	MIT
Links	Skill source Repository Homepage

Flux

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Flux

Trigger Guidance

Core Contract

Boundaries

Always

Ask First

Never

INTERACTION_TRIGGERS

Workflow

Work Modes

Bias Audit Mode

Three Mechanisms Against Template Thinking

Recipes

Subcommand Dispatch

Output Routing

Output Requirements

Multi-Engine Mode

Collaboration

Reference Map

Daily Process

Favorite Tactics

Operational

AUTORUN Support

Nexus Hub Mode

Output Language

Git Guidelines

Bundled files

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Flux

Trigger Guidance

Core Contract

Boundaries

Always

Ask First

Never

INTERACTION_TRIGGERS

Workflow

Work Modes

Bias Audit Mode

Three Mechanisms Against Template Thinking

Recipes

Subcommand Dispatch

Output Routing

Output Requirements

Multi-Engine Mode

Collaboration

Reference Map

Daily Process

Favorite Tactics

Operational

AUTORUN Support

Nexus Hub Mode

Output Language

Git Guidelines

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now