Fixation is the default state. When a generator (human or LLM) has been working on a problem, attention concentrates on the current framing and subsequent ideas tend to be local variations on it. This skill is the interrupt: spend one move stepping sideways, then resume.

The discipline matters more than the move. Pick ONE technique per invocation, run it without second-guessing, and produce explicit reframings or candidate entry points — not a brainstorm list in the same frame.

Claude activates this skill when:

Do NOT activate this skill as a default before every consequential task — fixation is the trigger, not stakes. If the current frame is working, let it work.

Three rules that apply across every move below. Violations make the output ideation-flavored but structurally identical to what came before.

Stop condition. Stop when the move has produced 3+ non-trivial framings, or a single framing that reorganizes the problem (one sharp surprise beats five adjacent). Do not keep generating because the list looks short — volume is not the product.

The fire test. After every move, ask: could this output have been produced without the move? If yes, the move did not fire. Either commit harder (push the provocation further, make the reframe more aggressive, re-roll the random word, invert on a different axis) or the move was mismatched to the stuck-pattern — re-diagnose and pick the better-matched move. Re-diagnosis after a miss is not menu-rotation; menu-rotation is cycling through techniques without commitment. One move at a time, each one fully, and if it misses, diagnose why before the next.

Tail sift. The fire test with a number attached. Post-trained models carry a typicality bias: asked for one candidate they return the modal one, and asked for a list they return the top-k modes — a bestseller list, not a sample (Zhang et al. 2025). The 3 framings a move produces are subject to this too. So before evaluating, over-generate — 5 to 8 candidates rather than 3 — and write next to each one the probability, 0 to 1, that a generator still inside the old frame would have produced it. Drop everything above the threshold; the remainder is the move's actual output. Start the threshold at 0.3 and lower it if the survivors still read as adjacent. Writing the number is the mechanism, not decoration: prompts that request a distribution with verbalized probabilities recover diversity that prompts for instances or lists do not, quality holds when the candidates are reasoned rather than listed, and lowering the stated threshold moves output further into the tail. The gain scales with model capability; on a small model the sift adds burden without adding distance.

Match the stuck-pattern to the move. When unsure, default to Reframe.

The problem-as-stated is rarely the problem-to-solve. Mutate the sentence:

Produce 3 reframings. Fired if: at least one makes the original statement sound naive, or shifts who owns the problem.

Edward de Bono's method. Prefix a deliberately wrong, impossible, or absurd statement with to signal it is not a proposal — it is a stimulus. Then extract movement: what principle, consequence, or adjacent idea does this surface?

Four recipes (de Bono's formal provocations):

The canonical example: a factory pollutes a river. Po: the factory is downstream of itself. Impossible, but it generates: move intake downstream of discharge. Internal incentive to not pollute. Closed-loop water. The provocation is discarded; the movement stays.

Fired if: the provocation is genuinely impossible or absurd (not merely edgy), AND extracting movement yields a principle that survives translation back to the real constraints. If the "provocation" is a thing you could actually do, it's a proposal, not a Po — push it further.

Pick a word, object, or domain with no connection to the problem. Force a connection. The forced-feel is the point — it routes around the habituated pathway.

Template: "How is [problem] like [random]?" then "What does that suggest?"

Sourcing for humans: a random Wikipedia article, a nearby physical object, an Oblique Strategies card, a concept from an unrelated field on the current desk. Commit to the first thing you land on; re-rolling defeats the method.

Sourcing for an LLM agent: an LLM asked directly for a random word does not produce one — the same attention that locked the frame picks a word adjacent to it, and post-training biases the pick toward whatever is typical. Two sources work:

Fired if: the connection is genuinely forced (the first 10 seconds feel wrong), and working through the force produces an angle that was not in your prior search space. If the random word feels "relevant" immediately, you re-rolled, picked from attention, or read one character of the seed — get a new one.

For iterating on an existing artifact. Walk the seven prompts once; do not pick favorites in advance.

Fired if: at least one prompt produced a candidate you would not have reached by asking "what's a better version of this?". If all seven outputs are adjacent polish, the artifact is not the unit of analysis — zoom out and try Reframe.

Solve the inverse problem, then negate the solution. Works because failure modes are often more concrete than success paths.

Fired if: inverting surfaced a concrete risk, mechanism, or incentive the forward framing was hiding. If negating the inverted answer gives you the same thing you already had, the inversion was too symmetric — invert on a different axis (goals → incentives, success → unobservable, user → operator).

Move the problem into a distant domain by its relational structure, not by asking the distant domain a question. "How would biology solve this?" is the cross-domain baseline in Shen, Druckmann & Zou (2026), and it collapses almost as hard as no domain prompt at all: across 150 generations per problem, about 5% of proposed domains were unique, and solution diversity (Vendi score) was 8.3 against 5.8 for the unconstrained baseline. Explicit structure-mapping scored 90–173% higher on solution diversity, produced solutions judged novel 50–69% of the time against 1.6–38% for the baselines, and reached domains further from the problem. The written mapping is what changes the outcome.

Four steps, after Gentner's structure-mapping theory:

Domain menu, when step 2 needs a starting point: natural (biology, ecology, geology), trade (kitchen, ER triage, shipping dock), role (CFO, child, historian, adversary), scale (100x, 1/100x).

Fired if: the mapped domain is one the problem's own literature would not cite, AND step 3 returned a named, existing method — not a metaphor. If the object pairs match by resemblance rather than role, or the "method" is the original problem restated in new nouns, the mapping was surface-level; redo step 2 with a further domain.

Constraints define the solution space. Move them deliberately.

Fired if: a relaxed-constraint solution reveals what you actually value (not just what you'll accept), or an added-constraint solution is sharper than the unconstrained one. If both feel like the same answer with a different budget, the binding constraint is elsewhere — find it.

For when you hold two (or more) related instances and generation keeps orbiting them. The pair is not the object — the parametrized family containing both is. Sub-moves in descending observed yield:

Then sharpen and verify: the traversal's real product is questions precise enough to have derivable answers; the discovery happens in the derivation and a cheap measurement, not in the geometry. (A frontier framing of an addressing scheme implied a capacity law, j² ≤ 2^(significand bits); sixty seconds of numpy confirmed a cliff at exactly N = 4096, closing a four-month-old empirical mystery.) Without this step the move outputs taxonomy, not generation.

Fired if: a limit point or constraint-swap landed outside the prior search space, AND at least one output is a checkable claim. If the output is only a tidy classification of the anchors, the move stalled — push further along the edge or swap the constraint.

Caution: do not equate the interior of the space with novelty. In high dimensions essentially all operation is already extrapolation outside the training hull (Balestriero & LeCun 2021), and mixtures of known points are averages. The generative directions are the limits and the constraint-swaps; the chord is a probe, not a doctrine.

LLM agents exhibit a context-bound analog of functional fixedness: attention concentrates on current framing and generates variations of it. A second mechanism compounds it and does not depend on context: preference data favors familiar text, so post-training sharpens the policy toward the modal continuation for any prompt (Zhang et al. 2025, Theorem D.1) — which is why "give me five alternatives" returns five near-neighbors even in a fresh context. Signals this is happening:

When detected, the fix is the same: pick one move from the diagnostic table, execute it on the agent's own current framing, and explicitly write out the new framing(s) before resuming work. The write-out is essential — a framing that stays implicit in attention gets re-absorbed into the previous frame.

Load these only when the user wants depth on a specific technique.