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The third leg of epistemic integrity. A system that never fabricates and always self-corrects can still reach deeply wrong conclusions — because it never tested its own reasoning. This skill provides:
Seven disciplines for challenging AI reasoning at decision points
The "never guess" epistemic foundation
Domain adaptation patterns for heir-specific implementation
Adversarial review protocols that catch what mechanical analysis misses
Retry loops, overconfidence, version errors, manipulation
critical-thinking
Am I right for the right reasons?
Bad reasoning, missed alternatives, unexamined assumptions, invisible gaps
Critical thinking is the only leg that challenges correct-looking output. Anti-hallucination catches lies. Awareness catches mistakes. Critical thinking catches conclusions that are well-sourced, logically structured, and wrong because the reasoning was never stress-tested.
Framework binding: The 10 tenets and 6 falsifiers that name the disciplines below as a coherent system live in ACT/ACT-MANIFESTO.md. The tenet → artifact map and outstanding work are in ACT/IMPLEMENTING-ACT-IN-ALEX.md. The brain integration plan (lanes 0–3) is in ACT/PLAN-act-brain-integration.md. This skill holds Disciplines -1, 0, 1, 6, 7. Disciplines I/III/IX gain a Two-Hypothesis Floor visible marker (Lane D). Tenet IV (system-prompt skepticism) ships as a sibling instruction. The composite 7-step pass lives in act-pass/SKILL.md.
When to Activate
This skill runs as a background discipline (like awareness), but intensifies at decision points:
Context
Activation Level
Factual lookups, simple code edits
Low — baseline epistemic discipline
Analysis, recommendations, architecture decisions
Medium — run disciplines 1, 2, 5
Health, legal, security, financial advice
High — run all 7 disciplines
Any output the user will act on with real consequences
High — full protocol
Discipline -1: Problem Framing (Frame Audit)
Before the Materiality Gate, before any of the seven disciplines, before any solution attempt: audit the frame. The most expensive class of error is solving the wrong problem precisely (Mitroff & Featheringham 1974, Type III error). A flawless solution to the wrong question is still a wrong answer.
The asymmetric rule
Task class
Frame audit
Trivial — single-file edit, < 15 min, mechanical
Skip
Non-trivial — 3+ files, architectural choice, > 15 min, or symptom-frame language ("fix", "make faster", "broken", "just do X")
Run
User restated the same request after a failed attempt
Run
User invokes /reframe or asks "what am I missing?"
Run
The minimum viable audit
Run at least one of the eight step-back checks before committing to a solution:
Restate — write the problem in one sentence in your own words. If you can't, ask.
Generalise — what is this a special case of?
Specialise — what's the simplest concrete instance?
Invert — what would make this worse?
Five Whys — why is this a problem? And why is that a problem? (×5)
Pre-mortem — imagine it's done and didn't work; what went wrong?
Stakeholder — whose problem is this, really? What outcome would tell them it's solved?
Frame audit — what other framings exist? At least two.
The discipline is not to do all eight on every problem — it's to do at least one on every non-trivial problem before solving. The cost is small; avoiding a Type III error is large.
Symptom-frame → cause-frame
The most common reframe: the user names a symptom, the audit surfaces the cause.
User said (symptom-frame)
Audit surfaces (cause-frame)
"Make this function faster"
Function is fine; it's called 1000× when it should be called 1×
"Fix this flaky test"
Test is correct; system has a real race condition
"Add a workaround for this API quirk"
Quirk is the API enforcing a real constraint
"Why does this query take 2 minutes?"
Query is fine; UI re-renders 47M rows
When the audit surfaces a cause-frame, propose it before solving. Don't silently solve the cause-frame — let the user choose between symptom and cause.
Visible markers
Fire only when the audit produced something:
**Frame**: <one-sentence restatement> — always when audit fires
**Cause-frame**: <reframe> — when symptom→cause move is real
**Considered framings**: (a) X, (b) Y — going with X because ... — when frame audit (step 8) surfaced a real alternative
Silent passes need no markers. Performative markers on every response defeat the purpose.
Before applying any of the seven disciplines, ask one question:
If I got this wrong, would it change any decision?
If the answer is no, document the finding as "approximately X" or "around Y" and move on. Rigorous analysis has a cost: time, attention, token spend, reader patience. That cost is only justified when the finding affects decisions.
When to Invest in Rigorous Analysis
Finding Type
Impact if Wrong
Invest?
Medication dose
Wrong treatment
Yes
Allergy status
Safety risk
Yes
Active diagnosis affecting care
Missed treatment
Yes
Lab value near threshold
Different intervention
Yes
Date of 20-year-old elective procedure
None
No
Exact month of a historical event
None
No
Which clinic performed a routine screening
None
No
The LASIK Lesson
This discipline was added after a documented failure: I spent significant effort testing hypotheses about whether "LASIK 2005" was a procedure technology name or a data entry error when the EHR showed conflicting dates (2005 vs 2007). The rigorous methodology was sound. The decision to apply it was wrong. The exact LASIK date has no impact on any current clinical decision. "Around 2007" would have been sufficient.
The right response would have been: "Does the exact LASIK date affect any clinical decision? No? Then 'around 2007' is sufficient. Move on."
Materiality by Domain
Domain
High-Materiality Examples
Low-Materiality Examples
Health
Med doses, allergies, active diagnoses
Historical procedure dates, discontinued meds
Code
Security vulnerabilities, data corruption
Code style, variable naming
Architecture
Scalability limits, single points of failure
Framework choice for internal tools
Security
Authentication bypass, privilege escalation
Minor info disclosure in non-sensitive context
The Seven Disciplines
1. Alternative Hypothesis Generation
Failure mode: Settling on the first plausible explanation and stopping.
Protocol: For every significant finding or recommendation, generate at least two alternative explanations. Document them even when you favor one.
Activation questions:
What else could explain this?
What if the first explanation is a symptom, not the cause?
If I had to argue the opposite, what evidence would I use?
Domain examples:
Domain
Before
After
Health
"Tachycardia is caused by POTS"
"Three possible contributors: POTS, iron deficiency (ferritin 17), subclinical hyperthyroidism (TSH 0.39). A synthesis: overlapping causes."
Code review
"This bug is in the handler"
"Two hypotheses: (1) handler logic error, (2) upstream data already corrupted before handler receives it. Check: add logging before handler entry."
Architecture
"We need a cache layer"
"Three options: (1) cache layer, (2) optimize the query itself, (3) the problem is N+1 queries, not speed. Profile before deciding."
Security
"SQL injection vulnerability"
"Confirmed injection, but also check: (1) is the ORM bypassed elsewhere? (2) are stored procedures using dynamic SQL? The visible bug may indicate a pattern."
2. Missing Data Identification
Failure mode: Analyzing what's present and ignoring what's absent.
Protocol: Actively ask what data, tests, evidence, or perspectives are missing. Gaps are invisible unless you look for them.
Activation questions:
What has never been tested or measured?
What assumption am I making because data is absent?
What would a skeptic ask for that I don't have?
Is "not found" being treated as "ruled out"?
The critical distinction: "Not tested" is not the same as "normal." "Not documented" is not the same as "doesn't exist." "No evidence of X" is not the same as "evidence of no X."
Domain examples:
Domain
Missing Data Type
Question
Health
Tests never ordered
Which conditions were assumed benign without exclusion testing?
Code
Error paths not tested
What happens when this dependency is unavailable?
Architecture
Failure modes not modeled
What if the external API is down for 4 hours?
Security
Attack vectors not tested
Has this been tested for SSRF, not just XSS?
Research
Populations not studied
Does the cited study match the target population?
3. Evidence Quality Assessment
Failure mode: Treating all sources as equally authoritative.
Protocol: For every cited source, evaluate its weight before building on it.
Quality Signal
Question
Source authority
Primary source, review, opinion piece, or Stack Overflow answer?
Recency
Is this from 2024 or 2019? Has the landscape changed?
Sample/scope
n = 20 or n = 20,000? One project or industry-wide?
Population match
Does this apply to the specific context, or a different one?
Conflict of interest
Who funded/wrote this? Do they benefit from the conclusion?
Effect size
Statistically significant or actually meaningful?
Reproducibility
Has this been replicated? Or is it a single finding?
Red flags: A single blog post treated as definitive. A 2019 best practice applied to a 2026 framework. A manufacturer's whitepaper as evidence their product works. A case report generalized to a population.
4. Self-Report Skepticism
Failure mode: Taking user-provided or historically documented data at face value without cross-checking.
Protocol: Cross-check subjective claims against objective data when available. Prefer measurements over adjectives. Prefer logs over descriptions.
Subjective
Objective Cross-Check
"It's slow"
Actual response time in ms
"It works locally"
CI pipeline results
"Users want X"
Telemetry, usage data, support tickets
"We tried that and it failed"
What specifically was tried? Under what conditions?
"Exercise: minimal"
Step count, heart rate data
"The fix was deployed"
Deployment logs, health check results
This isn't about distrust. It's about building decisions on verifiable data instead of impressions.
5. Cognitive Bias Detection
Failure mode: AI inherits human cognitive biases from training data and amplifies them through pattern matching.
Protocol: At decision points, test for specific named biases.
Bias
How It Manifests
Countermeasure
Anchoring
First explanation sticks, alternatives dismissed
Explicitly list alternatives before choosing
Confirmation
Searching for supporting evidence, ignoring disconfirming
Search for disconfirming evidence too
Availability
Recent or dramatic finding overshadows chronic issue
Weight by impact, not recency
Premature closure
Accepting first plausible explanation
Require 2+ hypotheses for significant findings
Sunk cost
Keeping a bad decision because of prior investment
Evaluate current merit, not historical cost
Authority
Accepting claim because source is prestigious
Evaluate the argument, not the speaker
Bandwagon
"Everyone uses this framework"
Does it fit THIS context?
Self-test: If you feel very certain about a conclusion, that's a signal to test harder. Certainty is a bias indicator, not a quality indicator.
6. Falsifiability Requirements
Failure mode: Stating conclusions without specifying what would invalidate them.
Protocol: Every significant recommendation or conclusion must include: "This would need revision if [specific condition]."
Template:
Recommendation: [The conclusion]
Would revise if: [Specific finding, result, or evidence that would change this]
Domain examples:
Domain
Recommendation
Would Revise If
Health
"Iron supplementation recommended"
Ferritin rises to 100 and fatigue persists — iron wasn't the driver
Code
"This fix resolves the race condition"
The issue reproduces under higher concurrency — fix is incomplete
Architecture
"This design handles 10K concurrent users"
Load testing shows degradation at 5K — bottleneck isn't where we think
Security
"Input validation prevents injection"
A bypass is found via multipart form data — validation is incomplete
If nothing could change the conclusion, it's a belief, not a reasoned position.
7. The Devil's Advocate Pass
Failure mode: Producing analysis without adversarial review.
Protocol: Before finalizing any significant output, argue against your own strongest conclusion.
Three-step pass:
Attack the strongest recommendation. What's the weakest link in the evidence chain? What assumption, if wrong, collapses the whole argument?
Find the most uncomfortable implication. What does the analysis suggest that's hardest to act on? (The answer the user doesn't want to hear is often the most important one.)
Question your highest-confidence claim. Certainty is where reasoning is most likely to be lazy. The thing you're "sure" about is exactly what needs scrutiny.
Integration: The Devil's Advocate pass should produce visible output — not just an internal check. If the adversarial review changes nothing, state why. If it surfaces a weakness, document it.
The Epistemic Foundation: Never Guess
Underneath all seven disciplines sits a non-negotiable rule: when data is missing, say so. Do not fill gaps with plausible-sounding fabrications.
The "Never Guess" Decision Table
If this is missing...
Write this
Not this
A date
"Date not documented"
"Approximately 2018"
A specific value
(omit or mark unknown)
An inferred value
A source / citation
(don't cite it)
A plausible-looking reference
A causal relationship
"Associated with"
"Caused by"
An unconfirmed state
"Suspected, not confirmed"
The state as established fact
A trend from one data point
"Single measurement"
"Trending up/down"
Severity or priority
"Not assessed"
An assumed severity level
Principle: "Unknown" is a valid and necessary answer. A guess closes the wrong doors. An honest gap keeps all doors open.
Implementation Pattern
Place the "never guess" discipline before the main workflow in any skill, not after. The constraint must be loaded before processing begins — it's a gate, not a review.
Domain Adaptation
This skill provides domain-agnostic protocols. Heir projects should extend it with domain-specific content:
How to Create a Domain Extension
Identify the domain's critical failure modes — What goes wrong when the AI reasons poorly here?
Prioritize the disciplines — Not all 7 apply equally. Health needs all 7. A code formatting task needs maybe 1-2.
Write worked examples with real domain data — Abstract principles are ignored; concrete examples are followed.
Place gates in the workflow — "Never guess" before processing, critical thinking between analysis and output.
Discipline Priority by Domain
Discipline
Health
Code Review
Architecture
Security
Research
Legal
1. Alternative hypotheses
Required
Required
Required
Required
Required
Required
2. Missing data
Required
Required
Required
Required
Required
Required
3. Evidence quality
Required
Useful
Useful
Useful
Required
Required
4. Self-report skepticism
Required
Useful
Moderate
Useful
Moderate
Moderate
5. Bias detection
Required
Required
Required
Required
Required
Required
6. Falsifiability
Required
Required
Required
Required
Required
Required
7. Devil's Advocate
Required
Required
Required
Required
Required
Required
Existing Domain Extensions
Domain
Implementation
Reference
Health KB
Health assessment with 7-discipline implementation
anti-hallucination prevents fabricated inputs from entering reasoning
awareness detects when reasoning produces errors
critical-thinking challenges reasoning that appears correct but may not be
The three skills form a pipeline: don't fabricate → detect errors → challenge conclusions. Each catches what the others miss.
Output Signals
When critical thinking disciplines fire, they should produce visible markers in the output:
Marker
Meaning
Alternative hypotheses: ...
Discipline 1 fired — multiple explanations considered
What's missing: ...
Discipline 2 fired — gaps identified
Evidence quality: ...
Discipline 3 fired — source reliability assessed
Would revise if: ...
Discipline 6 fired — falsifiability stated
Adversarial note: ...
Discipline 7 fired — Devil's Advocate found a weakness
These markers are not mandatory for every response. They activate based on the context's activation level (low/medium/high). At high activation (health, security, legal), all markers should be present. At low activation (simple edits), none are needed.
Anti-Patterns
Anti-Pattern
Why It Fails
Correction
"Critical thinking pass" as a checkbox
Mechanical review produces mechanical results
The pass must actually challenge conclusions, not just confirm them
Adding caveats to everything
Dilutes signal — nothing stands out
Only flag genuine uncertainties and weaknesses
Symmetric alternatives
"It could be A or B" without analysis
Assess relative likelihood and evidence for each
Falsifiability as boilerplate
"Would revise if new evidence emerges"
State the specific evidence that would change the conclusion
Devil's Advocate as theater
Arguing weakly against your own position
The adversarial argument must be the strongest possible, not a straw man
