feat(skills): add deep-research skill

Copy deep-research skill from local Qoder installation to config repo for version control

skills/deep-research/references/logical_fallacies.md

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+# Logical Fallacies Catalog — 30+ Fallacies for Research Review
+
+## Purpose
+Reference catalog of logical fallacies commonly encountered in research. Used by the devils_advocate_agent.
+
+## Formal Fallacies (Invalid Logical Structure)
+
+### 1. Affirming the Consequent
+**Structure**: If P then Q; Q is true; therefore P is true.
+**Example**: "If a university has high research funding, it has good outcomes. This university has good outcomes. Therefore, it must have high research funding."
+**Problem**: Q can have multiple causes.
+
+### 2. Denying the Antecedent
+**Structure**: If P then Q; not P; therefore not Q.
+**Example**: "If enrollment increases, revenue increases. Enrollment didn't increase. Therefore, revenue didn't increase."
+**Problem**: Revenue can increase from other sources.
+
+### 3. Undistributed Middle
+**Structure**: All A are B; All C are B; therefore All A are C.
+**Example**: "All successful programs use technology. Our program uses technology. Therefore, our program is successful."
+**Problem**: B (technology use) is shared but doesn't link A and C.
+
+### 4. False Dilemma / False Dichotomy
+**Structure**: Either A or B; not A; therefore B.
+**Example**: "Either we adopt online learning completely or maintain traditional methods."
+**Problem**: Many hybrid options exist.
+
+## Informal Fallacies
+
+### Relevance Fallacies
+
+### 5. Ad Hominem
+**Description**: Attacking the person rather than the argument.
+**Research Example**: "This study's conclusions are unreliable because the author works for a for-profit university."
+**Correct Approach**: Evaluate the methodology and evidence, not the author's affiliation (though COI should be noted).
+
+### 6. Appeal to Authority
+**Description**: Accepting a claim solely because an authority figure endorses it.
+**Research Example**: "Published in Nature, so the findings must be valid."
+**Correct Approach**: Even prestigious journals publish flawed studies. Evaluate on merit.
+
+### 7. Appeal to Tradition
+**Description**: Arguing something is correct because it has always been done that way.
+**Research Example**: "This metric has been used for 30 years, so it must be the best measure."
+**Correct Approach**: Evaluate whether the metric is still valid in current context.
+
+### 8. Appeal to Novelty
+**Description**: Arguing something is better because it's new.
+**Research Example**: "This new framework must be superior to the established one."
+**Correct Approach**: Novelty doesn't equal improvement. Compare on evidence.
+
+### 9. Appeal to Popularity (Bandwagon)
+**Description**: Arguing something is true because many people believe it.
+**Research Example**: "Most researchers in the field use this method, so it must be the best."
+**Correct Approach**: Popularity doesn't validate methodology. Assess independently.
+
+### 10. Red Herring
+**Description**: Introducing an irrelevant topic to divert from the argument.
+**Research Example**: Responding to criticism of methodology by discussing the importance of the topic.
+
+### Evidence Fallacies
+
+### 11. Cherry-Picking (Selection Bias)
+**Description**: Selecting evidence that supports the conclusion while ignoring contradictory evidence.
+**Research Example**: Citing 5 studies that support the hypothesis while omitting 12 that don't.
+**Detection**: Compare cited sources against comprehensive search results.
+
+### 12. Confirmation Bias
+**Description**: Seeking, interpreting, and remembering information that confirms pre-existing beliefs.
+**Research Example**: Designing search terms that are more likely to return supportive results.
+**Detection**: Check if search strategy was neutral; look for actively sought disconfirming evidence.
+
+### 13. Survivorship Bias
+**Description**: Drawing conclusions only from "survivors" (successes), ignoring those that didn't survive.
+**Research Example**: "All top-ranked universities implemented X" — ignoring universities that implemented X and didn't improve.
+**Detection**: Ask "what about those that failed?"
+
+### 14. Anecdotal Evidence
+**Description**: Using individual stories as proof of a general claim.
+**Research Example**: "One university tripled enrollment after rebranding, so rebranding drives enrollment."
+**Detection**: Is this a systematic finding or an isolated case?
+
+### 15. Hasty Generalization
+**Description**: Drawing broad conclusions from insufficient evidence.
+**Research Example**: "Three case studies from Taiwan show X, therefore this applies to all Asian universities."
+**Detection**: Is the sample representative? Is the generalization proportionate to the evidence?
+
+### Causal Fallacies
+
+### 16. Post Hoc Ergo Propter Hoc
+**Description**: Assuming that because B followed A, A caused B.
+**Research Example**: "After implementing the new curriculum, graduation rates improved. Therefore the curriculum caused the improvement."
+**Detection**: Were there confounders? Was there a control group?
+
+### 17. Cum Hoc Ergo Propter Hoc (Correlation ≠ Causation)
+**Description**: Assuming that correlation implies causation.
+**Research Example**: "Universities with more international students have higher rankings, so international students cause higher rankings."
+**Detection**: Is there a plausible mechanism? Could both be caused by a third factor?
+
+### 18. Reverse Causation
+**Description**: Getting cause and effect backwards.
+**Research Example**: "Good facilities attract students" when actually "student fees fund better facilities."
+**Detection**: Consider temporal order and alternative causal directions.
+
+### 19. Ecological Fallacy
+**Description**: Inferring individual-level conclusions from group-level data.
+**Research Example**: "Countries with more education spending have higher GDP, so spending on education makes individuals richer."
+**Detection**: Are individual-level and group-level relationships the same?
+
+### 20. Simpson's Paradox
+**Description**: A trend present in subgroups reverses when groups are combined.
+**Research Example**: Department A and B both show improving retention, but the university overall shows declining retention (due to shifting enrollment proportions).
+**Detection**: Always check disaggregated data alongside aggregate.
+
+### Reasoning Fallacies
+
+### 21. Straw Man
+**Description**: Misrepresenting an opponent's argument to make it easier to attack.
+**Research Example**: Critic says "this method has limitations" → Author responds "my critic says the entire study is worthless."
+**Detection**: Does the refutation address the actual criticism?
+
+### 22. Moving the Goalposts
+**Description**: Changing the criteria for success after seeing results.
+**Research Example**: Defining "program success" as enrollment growth, then shifting to "student satisfaction" when enrollment drops.
+**Detection**: Were success criteria pre-defined?
+
+### 23. Slippery Slope
+**Description**: Arguing that one action will inevitably lead to an extreme outcome.
+**Research Example**: "If we allow flexible admission criteria, academic standards will collapse entirely."
+**Detection**: Is each step in the chain actually probable?
+
+### 24. Circular Reasoning (Begging the Question)
+**Description**: The conclusion is assumed in the premise.
+**Research Example**: "This university is excellent because it is highly ranked, and it is highly ranked because it is excellent."
+**Detection**: Does the argument depend on the truth of what it's trying to prove?
+
+### 25. No True Scotsman
+**Description**: Redefining a category to exclude counterexamples.
+**Research Example**: "All quality assurance systems improve outcomes." "But system X didn't." "Well, X wasn't a true quality assurance system."
+**Detection**: Is the definition being modified to fit the claim?
+
+### 26. Equivocation
+**Description**: Using a term in two different senses within the same argument.
+**Research Example**: "Quality" used sometimes to mean "standards compliance" and sometimes to mean "student satisfaction."
+**Detection**: Is the key term defined consistently throughout?
+
+### Statistical Fallacies
+
+### 27. Base Rate Neglect
+**Description**: Ignoring the base rate (overall probability) in favor of specific information.
+**Research Example**: "This program has a 90% satisfaction rate" — but the base rate for all programs is 88%.
+**Detection**: Always compare against relevant base rates.
+
+### 28. Regression to the Mean
+**Description**: Extreme performances naturally tend back toward average on subsequent measurements.
+**Research Example**: "Our intervention improved scores for the lowest-performing students" — they may have improved anyway.
+**Detection**: Was there a control group? Were initial measurements extreme?
+
+### 29. Texas Sharpshooter
+**Description**: Finding a pattern in random data by focusing on clusters and ignoring misses.
+**Research Example**: Running 20 statistical tests and reporting only the 1 that was significant.
+**Detection**: Were hypotheses pre-registered? Was multiple testing corrected for?
+
+### 30. Gambler's Fallacy
+**Description**: Believing past random events influence future random events.
+**Research Example**: "This institution has declined for 5 years, so it's due for improvement."
+**Detection**: Is there a causal mechanism for reversal, or is this just pattern-seeking?
+
+### 31. McNamara Fallacy (Quantitative Bias)
+**Description**: Making decisions based solely on quantitative metrics while ignoring qualitative factors.
+**Research Example**: Ranking universities only by publication counts, ignoring teaching quality and community impact.
+**Detection**: Are important but hard-to-measure factors being excluded?
+
+### 32. Goodhart's Law
+**Description**: "When a measure becomes a target, it ceases to be a good measure."
+**Research Example**: Universities gaming rankings metrics instead of genuinely improving quality.
+**Detection**: Has the metric become a target? Are there signs of metric manipulation?
+
+## Quick Reference: Detection Questions
+
+| Ask This | Detects |
+|----------|---------|
+| "Does B have other possible causes?" | Post hoc, false cause |
+| "What about the failures?" | Survivorship bias |
+| "Is this sample representative?" | Hasty generalization |
+| "Were criteria defined before results?" | Moving goalposts, Texas sharpshooter |
+| "Is the key term used consistently?" | Equivocation |
+| "What's the base rate?" | Base rate neglect |
+| "What evidence was left out?" | Cherry-picking, confirmation bias |
+| "Is this the actual argument being made?" | Straw man |
+| "Can we distinguish correlation from causation?" | Cum hoc, ecological fallacy |
+| "Are individual and group levels being mixed?" | Ecological fallacy, Simpson's paradox |