Agent skill

cognitive-biases

Recognition and mitigation of systematic reasoning errors documented in the heuristics and biases tradition. Covers confirmation bias, availability, anchoring, representativeness, framing effects, hindsight bias, overconfidence, and motivated reasoning. Each bias is presented with its mechanism, diagnostic signal, and structured mitigation. Use when you suspect your own or another reasoner's conclusions may be shaped by systematic cognitive distortion rather than evidence.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill cognitive-biases-tibsfox-gsd-skill-creator --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 16 KB
Bundled scripts: none
Path: skills/analysis/cognitive-biases-tibsfox-gsd-skill-creator/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Cognitive Biases Human reasoning relies on heuristics — fast, frugal shortcuts that work well most of the time but produce systematic errors under predictable conditions. Cognitive biases are those systematic errors. They are not random noise; they are patterned departures from normative reasoning that can be anticipated, diagnosed, and partially corrected. This skill catalogs the twelve most consequential biases, each with a mechanism, a diagnostic signal, and a mitigation strategy. **Agent affinity:** tversky (heuristics and biases tradition), kahneman-ct (System 1 / System 2 framing), paul (integration with elements of reasoning) **Concept IDs:** crit-confirmation-bias, crit-availability-anchoring, crit-intellectual-humility, crit-calibrated-confidence ## The Bias Catalog at a Glance | # | Bias | Mechanism | Diagnostic signal | |---|---|---|---| | 1 | Confirmation bias | Seek and weight supporting evidence more than disconfirming | "I knew it" for every matching case; disconfirming cases feel like "exceptions" | | 2 | Availability heuristic | Judge probability by how easily examples come to mind | Vivid recent events dominate risk estimates | | 3 | Anchoring | First number or

What's inside
Steps it walks through
  1. The Bias Catalog at a Glance
  2. Bias 1 — Confirmation Bias
  3. Bias 2 — Availability Heuristic
  4. Bias 3 — Anchoring
  5. Bias 4 — Representativeness Heuristic
  6. Bias 5 — Framing Effects
  7. Bias 6 — Hindsight Bias
  8. Bias 7 — Overconfidence
  9. Bias 8 — Motivated Reasoning
  10. Bias 9 — Sunk Cost Fallacy
  11. Bias 10 — Fundamental Attribution Error
  12. Bias 11 — In-Group Favoritism
  13. Bias 12 — Base Rate Neglect
  14. Integrating the Biases into Practice
Ships with 1 file
  • metadata.json
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About this skill
What does the cognitive-biases skill do?

Recognition and mitigation of systematic reasoning errors documented in the heuristics and biases tradition. Covers confirmation bias, availability, anchoring, representativeness, framing effects, hindsight bias, overconfidence, and motivated reasoning. Each bias is presented with its mechanism, diagnostic signal, and structured mitigation. Use when you suspect your own or another reasoner's conclusions may be shaped by systematic cognitive distortion rather than evidence.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill cognitive-biases-tibsfox-gsd-skill-creator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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