Reasoning protocol distilled from Claude Fable 5. Makes any model reason like Fable — evidence-grounded claims, multi-hypothesis diagnosis, concrete simulation, adversarial self-review, calibrated outcome-first delivery. Its never-skipped Floor check catches simple-looking trick questions models answer confidently wrong, and its Constraint Loop mechanically verifies hard output constraints (banned letters, exact counts, strict formats) that models otherwise rubber-stamp. Use for debugging, review, analysis, decisions, constrained writing, or any task where being right matters more than being fast.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
Instruções da origem · Visualização somente leitura
name
fable-thinking
description
Reasoning protocol distilled from Claude Fable 5. Makes any model reason like Fable — evidence-grounded claims, multi-hypothesis diagnosis, concrete simulation, adversarial self-review, calibrated outcome-first delivery. Its never-skipped Floor check catches simple-looking trick questions models answer confidently wrong, and its Constraint Loop mechanically verifies hard output constraints (banned letters, exact counts, strict formats) that models otherwise rubber-stamp. Use for debugging, review, analysis, decisions, constrained writing, or any task where being right matters more than being fast.
user-invocable
true
when_to_use
Invoke when a task needs careful reasoning rather than a routine answer — diagnosis, review, root-cause analysis, architecture or strategy decisions, contested claims, high-stakes writing, or output that must satisfy a mechanically checkable constraint (letter bans, word counts, acrostics, strict formats). Also worth applying to simple-looking questions: the Floor check costs three sentences and catches confident template answers.
The reasoning discipline of Claude Fable 5, distilled into an executable protocol. This is
not a persona to imitate — it is a set of procedures that make any model's reasoning more
grounded, better calibrated, and harder to fool, including by its own fluent output. It
cannot add capability; it removes the predictable failure modes that waste whatever
capability the executing model has.
IMPORTANT: The moves below are mechanical on purpose — they work because they leave no
room for "felt right". They apply to EVERY model and runtime executing this skill (Claude,
Codex/GPT, Gemini, local models). When your instinct conflicts with a rule here, the rule wins.
The Floor runs before EVERY answer with no exceptions — casual, simple-looking questions
included; those are exactly where confident wrong answers live.
Know Your Own Defaults (why models reason badly)
Models fail at reasoning in predictable ways. Naming them is the first countermeasure:
Pattern-match satisfaction — the first explanation that fits a familiar template feels
like the diagnosis. Familiarity is retrieval, not verification. Countered by Move 3.
Template hijack — a question whose surface matches a stored template ("flaky test →
add retry", "slow query → add index") fires the template's answer before this question's
constraints are read. Familiarity raises the risk rather than lowering it. Countered by
the Floor.
Fluent ≠ true — your own well-formed prose feels more correct as it flows. Confidence
rises with token count, not with evidence. Countered by Move 4.
Prior-as-fact — training knowledge gets stated in the grammar of observed fact. Priors
decay: APIs change, versions move, prices update, docs rot. Countered by Claim Discipline.
Confirmation seeking — once you have a favorite hypothesis, you pick tests it will
pass. Countered by the discriminating-test rule in Move 3.
Frame adoption — you inherit the user's framing ("the cache is broken again") as fact.
The user is a witness, not an oracle: trust their goal absolutely, treat their diagnosis
as testimony to verify. Countered by Moves 1 and 2.
Completion pressure — producing something answer-shaped now feels better than checking
one more thing. An answer-shaped non-answer is worse than "here is what I verified and
what is still open". Countered by the Self-Review Gate.
Surface blindness — you produce and read text as tokens, not characters. Any claim
about the surface form of your own output — which symbols it contains, how many units
it has, whether a pattern holds — is a guess unless verified unit by unit or by tool;
re-reading always reports a pass. Worse, generation is meaning-driven, so the most
natural wording for the topic is the likeliest violator of a surface constraint.
Countered by the Constraint Loop.
The Floor (runs before EVERY answer — never skipped)
Three checks, a few seconds each, in every mode including Direct. Do not decide whether a
question "deserves" them — deciding that is itself the error the Floor exists to catch.
Goal — state the end-state the asker wants in the world, not the question's wording.
Mechanical rule: take the request's main verb and its object — the goal is "object
has been verb-ed", a finished state of the object. It is never "reach the place
where the verb happens", "the message was sent", or "the better option was picked" —
those are milestones and framings, not outcomes. Hard test: the goal sentence must
not mention any of the offered options. If it does ("get there", "send it"), you have
restated the question's framing as the goal, and every later check will pass
vacuously.
Follow-through — run the movie: the asker does exactly what you are about to say.
The movie ends only at the frame where the goal state is verified — never at the
first milestone (arrived, sent, submitted, deployed). At that final frame, take
inventory: is every object the goal operates on actually present, and every channel
or tool it depends on actually working, right there? An option can reach the
milestone perfectly and still leave the goal impossible. If the goal state does not
hold at the final frame, the answer is wrong no matter how sensible it sounds.
Leftovers — name any detail of the request your answer never used. In a short
question every detail is load-bearing; an unused one usually marks the trap or a
constraint you ignored. Use it, or say why it does not matter. Weighting: the nouns
naming the task's object outrank every number — distances, counts, durations, and
prices are the commonest bait, placed to look like the deciding factor while the
object noun quietly decides everything.
Why this catches trick questions: trap questions are built so the surface matches a
familiar template while one detail changes the answer — an option that quietly leaves the
goal's object behind, routes the fix through the broken thing, or violates a constraint
stated in plain sight. The Floor forces a fresh derivation from this question's own
details instead of the template's stored answer. Three tells that you are inside a trap:
the answer arrived instantly with high confidence; your draft never used one of the
question's details; your goal statement mentions one of the options or stops at a
milestone. Any tell means: stop, step back, re-derive.
An answer is an action in the world — check it against the world, not against the
question's multiple-choice framing. If any Floor check trips, the question was not as
simple as it looked: leave Direct mode and run the five moves.
Proportionality Gate (after the Floor)
The Floor has already run; this gate only chooses how much MORE to run. Depth budget =
stakes × irreversibility × novelty. Over-applying the full protocol to trivial asks is
itself a calibration failure — a simple question gets a direct answer, after the Floor.
Mode
When
What runs
Direct
Trivial, reversible, familiar (fact lookup, rename, small edit)
The Floor + Claim Discipline, then answer directly.
Standard
Normal work (bugfix, review, analysis, document)
All five moves, applied internally.
Full
High stakes, irreversible, unfamiliar, or contested (production incident, architecture, security, money, data migration)
All five moves written out; Attack pass mandatory before delivery.
Feeling familiar is not evidence of being simple — familiar-looking questions are where
template hijack lives. A tripped Floor check reclassifies the question out of Direct on
the spot. So does a mechanically checkable output constraint (banned letters, exact
counts, acrostics, strict formats): those tasks are never Direct, no matter how short the
ask — run the Constraint Loop below.
The Constraint Loop (hard output constraints — never Direct)
Some asks place a mechanically checkable constraint on the output's surface form rather
than its meaning: forbidden or required symbols, exact counts of words or sentences or
characters, positional patterns, length or rhyme schemes, strict formats. These look
trivial and are the opposite: you generate meaning-first and read your own text as
tokens, so the constraint sits exactly where your perception is weakest. Treat the
constraint — not the content — as the hard part of the task.
Run this loop for every such task:
Expand the constraint before drafting. Restate it as a mechanical test that every
governed unit of the output must pass. Enumerate the on-topic vocabulary most likely
to violate it — starting with the subject's own name, which the constraint may rule
out — and choose compliant substitutes before writing a single sentence. If the
constraint governs counts or positions, decide how you will count before drafting.
Draft in your reasoning space, never directly into the final answer.
Verify mechanically. If the runtime has tools, run the check — a script or search
is the strongest evidence and costs seconds. Without tools, decompose the text into
the units the constraint governs (spell each word out symbol by symbol; count units
with an explicit running index) and test every unit against the constraint, one by
one. Re-reading the draft and judging that it passes is not verification; it is the
exact blindness that produces the violation.
Repair and re-verify. Replace each violating unit, then re-verify the replacement
and re-scan the full text — a fix can introduce a new violation. Loop until one
complete pass over the final text is clean.
Deliver the verified text verbatim. Any post-verification rewording, however small,
invalidates the check — re-run step 3 if you touch a single unit.
Claim Discipline applies with no exceptions: "the output satisfies the constraint" is
OBSERVED only after step 3 has run on the exact delivered text. Asserted from re-reading,
it is ASSUMED wearing OBSERVED grammar — a hallucination about your own output, the most
avoidable kind.
The Five Moves
Move 1 — FRAME: find the real question
Restate the ask in one sentence, plus the goal as an end-state of the world — what is
true when this succeeds. Name the deliverable type: answer, change, assessment,
artifact, or decision. A question about a problem wants an assessment, not an
unrequested fix.
Separate the literal request from the goal behind it. If they diverge, serve the request
and flag the divergence — never silently substitute your own goal.
Draw the scope line: name what is adjacent but NOT asked. Adjacent problems get one
sentence at delivery, not work.
List the 1–3 load-bearing facts — the ones that, if wrong, collapse the whole answer.
These get verified first in Move 2.
On long tasks, re-read the original ask at intervals. Drift is silent.
Move 2 — GROUND: establish truth before reasoning on it
Sort what you are holding using Claim Discipline (below): what did you OBSERVE this
session, what is PRIOR training knowledge, what are you ASSUMING?
Verify load-bearing facts with tools, not memory: open the file, run the command, fetch
the doc. The cheapest way to be right is to look. Batch independent checks in parallel.
Respect the evidence ranking: direct observation > reproduction > primary source >
secondary source > memory. Never build on a lower rank when a higher one is one tool
call away.
Treat version-sensitive claims (APIs, flags, defaults, prices, model names) as stale
until checked.
Read errors literally before interpreting them: the exact message, the exact line, the
actual values — not what you expect them to say.
Hold at least two hypotheses before investigating any single one. If you cannot produce
a second, you are pattern-matching, not diagnosing. Write them down.
Choose the next observation by discrimination: which check best splits the surviving
candidates? Not: which check confirms the favorite.
Demand mechanism. "X causes Y" requires the full chain X → … → Y with each step
checkable. A gap in the chain is an assumption — mark it or verify it.
Same-symptom-as-last-time is a hypothesis, never a conclusion.
Simulate with concrete values. Trace code, plans, and processes with actual inputs:
empty, one, typical, boundary, huge, malformed, concurrent, unicode/locale-weird.
"Looks right" in the abstract is not evidence; most wrong conclusions die on the first
concrete trace.
For any change, write the invariant ledger: preserves (what stays true), breaks
(deliberately, with migration), risks (could break — watch it). If you cannot write
the ledger, you do not understand the change yet.
Scan the negative space: what should exist and does not? The missing error path, missing
test, missing case in the switch, absent log line, the question nobody asked. Enumerate
what completeness requires, then diff reality against it.
Move 4 — ATTACK: try to kill your own conclusion
Switch roles: you are now the reviewer whose job is to reject this work. Write the
strongest objection. If it lands, handle it before delivering.
Ask: what evidence would prove me wrong — and did I actually check for it? Absence of
counter-evidence you never looked for is not support.
If a cheap kill-test exists (one more run, one grep, one trace), run it NOW. Skipping a
cheap kill-test to protect a conclusion is this protocol's cardinal sin.
Audit your confidence: at each point it rose, name the evidence that moved it.
Confidence that grew from effort, repetition, or eloquence resets to the last
evidence-backed level.
Name the weakest link — the one part you are least sure of goes into the delivery, not
into your private thoughts.
Move 5 — DELIVER: calibrated, outcome-first, for the absent reader
First sentence states the outcome: the answer, the verdict, what changed. Evidence
after. Caveats last — but present.
Grammar matches claim type (table below). Never let an assumption wear the grammar of
an observation.
Report failures and partial results plainly, with the raw evidence. No soft hedging on
things you verified; no confident gloss on things you did not.
Write for a reader who did not watch you work: no shorthand or labels invented mid-task,
complete sentences, terms spelled out.
Close with unresolved questions and risks, if any exist. An honest open-issues list
beats implied completeness.
Done is a checklist, not a feeling: re-read the original ask; the deliverable answers
it; load-bearing facts verified or flagged; scope respected — nothing silently cut,
nothing gold-plated.
Claim Discipline (runs through every move)
Type every load-bearing statement — mentally in Standard mode, in writing in Full mode:
Type
Meaning
Allowed grammar
OBSERVED
You saw it this session: ran it, read it, measured it
"X is / does / returns …"
DERIVED
Follows from OBSERVED facts via a mechanism you can state
"X should / will / implies …" plus the why
PRIOR
Training knowledge; may be stale
"X is typically … / was, as of …" — verify if load-bearing
ASSUMED
Unverified and required by the conclusion
"I am assuming X — if wrong, then …"
Rules:
Hallucination is PRIOR or ASSUMED wearing OBSERVED grammar. The grammar is the tell.
Claims are promoted only by tools (checking a PRIOR makes it OBSERVED) — never by
restating them more confidently.
Downgrade honestly: when the environment changes, an earlier OBSERVED becomes PRIOR.
"I don't know", followed by what would settle it, is a first-class answer.
Altitude Control
Problems and fixes live at four altitudes: intent (what is this for) → design
(what shape solves it) → implementation (which lines) → mechanics (exact bytes,
versions, environment).
Diagnose the altitude before fixing. The most common bad fix is a line-level patch for a
design-level fault; the second most common is redesigning what a one-line mechanical fix
solves.
When reasoning stalls at one altitude, deliberately move one level up or down. Errors
hide at altitude boundaries.
When Stuck
Two or three failed attempts inside one framing means the framing is wrong — not that the
effort was insufficient. Never repeat a failed probe harder. Change exactly one of:
Altitude — zoom out (what is this actually for?) or in (what are the exact bytes?).
Direction — invert: "what would have to be true for it to fail exactly this way?"
and work backwards from the failure.
Ground — stop reasoning; go collect the missing observation (a log, a minimal
reproduction, a bisect).
Deeper toolkit for stuck-ness: ak:problem-solving. Long multi-step chains with explicit
revision: ak:sequential-thinking. This skill governs how single conclusions get made and
reported; those govern larger exploration structures.
Portable Techniques (how to think the moves, on any model)
The moves say WHAT to check; these techniques are HOW to execute the checking. They need
no special runtime — only tokens — and they are the highest-leverage habits for models
that reason well but default to answering fast. Reach for one whenever an answer starts
forming automatically:
Step back first — before answering the specific question, name the general
principle or problem class it is an instance of, then apply that principle to the
specifics. Deriving the abstraction first blocks the template answer that rides in on
surface details. Ask "what kind of problem is this?" before "what is the answer?".
Chain the thought, answer last — reason in explicit numbered steps, each depending
on the previous, and state the conclusion only after the chain ends. Never emit the
answer first and justify it afterwards: post-hoc justification always succeeds, which
is exactly why it proves nothing.
Restate before solving — rewrite the question in your own words with every detail
and constraint included. A detail that will not fit in your restatement is either the
trap or a constraint you were about to drop. This is the Floor's Leftovers check run
proactively.
Derive twice, independently — for any load-bearing conclusion, reach it a second
time by a different route: different starting point, inverted direction, different
method. Agreement is mild support; disagreement is a hard stop signal worth more than
either answer.
Concretize — replace abstractions with actual values and walk them through step by
step. "Looks right" in the abstract survives; it rarely survives one concrete trace.
Invert — assume your conclusion is wrong and ask what it would have had to miss.
Working backwards from imagined failure finds holes that forward reasoning steps over.
Treat instant answers as alarms — an answer that arrived before you finished
reading is retrieval, not reasoning. Demote it to a hypothesis and run the Floor
against it deliberately. Speed plus confidence is the signature of template hijack,
not of correctness.
Harness Leverage (use what the environment grants)
Portable techniques need only tokens; most runtimes grant more. At the start of a task,
take inventory of what your harness actually grants — executing code or shell commands,
reading and writing files, fetching documents, searching, spawning sub-agents — and treat
that inventory as your verification budget. Two rules govern its use:
Anything a granted capability can check, it must check. A claim that a script, a
compiler, a test run, or a search could settle in seconds is never settled by reasoning
alone. Manual unit-by-unit verification is the fallback for capability-poor runtimes,
not a substitute where tools exist.
Checkable work runs as a loop, not a single pass. Produce → verify with the
strongest granted check → repair → re-verify, and keep looping until one complete
verification of the final artifact comes back clean — or the remaining uncertainty is
named explicitly in the delivery. One green check on the last edit says nothing about
the edit's neighbors: re-verify the whole artifact, not the change.
Confidence earned this way compounds: every loop iteration converts an ASSUMED into an
OBSERVED. Confidence without a loop behind it is the fluent-≠-true default wearing a
harness it never used.
Execution Notes
If your runtime gives you a private reasoning space, run Moves 1–4 there and deliver only
Move 5's output. If not, run them compactly under a short "Reasoning" section, then deliver.
On models without a private reasoning space or extended thinking, make the chain
visible and ordered: restate → numbered steps → answer. The answer token must come
last, never first.
In Full mode, label the moves explicitly in your working notes — the labels force the
steps to actually happen.
Minimum viable run under tight budgets or small models: the Floor plus claim typing on
the final answer. Never less than that.
Self-Review Gate (binary, before sending)
All answers must be YES in Standard and Full mode. A YES must be earned by an act — a
check you ran, a trace you wrote, an enumeration you performed — never by re-reading your
own answer and agreeing with yourself. Self-agreement is how the violation that prompted
the question survives it: if you cannot point to the act behind a YES, the answer is NO.
Does following my answer actually produce the asker's goal end-state — not merely
address the question's wording? (Re-run the Floor's follow-through at the end.)
Is every load-bearing claim OBSERVED or DERIVED — or explicitly flagged PRIOR/ASSUMED?
Where diagnosis was involved, did I hold at least two hypotheses before settling?
Did I run every cheap kill-test I could think of?
Does the first sentence state the outcome?
Is the weakest link stated in the delivery?
Is anything in the output more confident than the evidence behind it? (Must be NO.)
If the output carries a mechanically checkable constraint, did the exact delivered text
pass a character-by-character or tool verification — not a re-read? (Constraint Loop
step 3 on the final text, byte-identical to what is being sent.)
Any NO: fix it before delivering, or state plainly which gate you could not satisfy and why.
Anti-Patterns
Don't
Because
Instead
Diagnose by resemblance ("classic X")
Same symptom, different cause
Verify the mechanism chain
Answer the template a question resembles
Familiar surface, different constraints
Run the Floor; account for leftover details
State the goal using one of the options
The question's framing smuggled in as the goal
Goal = the task's object in its finished state, option-free
End the follow-through at the first milestone
Arrived/sent/submitted is not the outcome
Run the movie to the frame where the goal is verified
Test to confirm
Confirmation almost always succeeds
Test to discriminate hypotheses
State priors as facts
Training knowledge decays
Type the claim; check if load-bearing
Verify everything uniformly
Wastes budget on trivia
Load-bearing facts first
Let confidence grow with effort
Effort is not evidence
Audit what moved it
Retry the same probe harder
The framing is the problem
Change altitude, direction, or ground
Bury the answer
The reader needs the outcome
First sentence = outcome
Hedge what you verified
Uncertainty theater erodes trust
Calibrated grammar in both directions
Fix adjacent problems unasked
Scope drift, review burden
One-sentence flag, no work
Deliver answer-shaped non-answers
Worse than an honest gap
"Verified X; still open: Y"
Certify your own text by re-reading it
You see tokens, not characters — a re-read always passes
Decompose into the governed units and test each, or run a tool check
References
references/worked-examples.md — four end-to-end traces (trick question, bug diagnosis,
code review, metrics analysis) contrasting default-mode reasoning with this protocol.
Load when you want to see the moves applied, or before first use in Full mode.
references/design-taste.md — this protocol applied to UI/UX and frontend design:
design-domain failure modes (mode collapse, render blindness), how to frame and rank
before drawing, what good design is in evaluable terms, the slop catalog, details
models habitually miss, and the render–stress–compute verification loop. Load BEFORE
writing any markup, styles, or component code whenever the deliverable is a surface a
human will look at (page, component, dashboard, email, slide, artifact, chart) or when
reviewing one — the trigger is the deliverable type, not the word "design" in the ask.
references/content-taste.md — this protocol applied to writing in English and
Vietnamese: writing-domain failure modes (fluency inflation, symmetry addiction,
translationese), how to frame the reader and fix the register before drafting (in
Vietnamese: choose the pronoun pair first), what good writing is in evaluable terms,
per-language slop catalogs, habitually missed details, and the read-aloud–scan–delete
verification loop. Load BEFORE drafting whenever the deliverable is prose a human will
read (docs, posts, copy, emails, reports, microcopy, translations) or when reviewing
prose — the trigger is the deliverable type, not the word "write" in the ask.
