Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add deciqAI/knowledge-skills --skill fundamental-attribution-errorgit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/deciqai/knowledge-skills/fundamental-attribution-error)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/fundamental-attribution-error"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/fundamental-attribution-error/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/fundamental-attribution-error"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/fundamental-attribution-error.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00125 | $0.01937 |
| Opus 5 | $0.00063 | $0.00968 |
| Sonnet 5 | $0.00025 | $0.00387 |
| Haiku 4.5 | $0.00013 | $0.00194 |
Grade A, and why
fundamental-attribution-error scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fundamental Attribution Error
Overview
The fundamental attribution error (FAE) is the systematic tendency to over-weight character factors and under-weight situational factors when explaining others' behavior — while reversing this for your own behavior (actor-observer asymmetry). Coined by Lee Ross (1977), grounded in Jones & Harris's 1967 Castro study. The bias is automatic (System 1); situational correction requires deliberate effort (System 2).
Composes with hanlons-razor, survivorship-bias, critical-thinking, dual-system-thinking, narrative-fallacy.
When to Use
- Diagnosing why an employee is underperforming
- Analyzing customer churn
- Conducting post-mortems on outages, accidents, or failures
- Mediating interpersonal conflict
- Designing products and observing user behavior
- Performance reviews, evaluations, competitor behavior analysis
- Someone says "fundamental attribution error," "actor-observer," or "what situation made this rational"
Not when: behavior shows a documented pattern across many distinct situations; situational framing is being used to avoid accountability.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a specific judgment to test for FAE → run The Process directly.
- Coach mode: user is unfamiliar or has no concrete case → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-line: before concluding someone behaved a certain way because of who they are, ask what situation would make the behavior rational — that's the FAE counter-move.
- Check fit: if the same person behaves the same way across many distinct situations, dispositional inference is stronger. Single-situation explanations are FAE-prone.
- Elicit the specific judgment. Who's the person? What's the behavior? What explanation is being offered?
[WAIT — do not advance until user responds]
- One question at a time: is the explanation dispositional? What's the situational alternative? If I were in that situation, would I have behaved differently?
[WAIT — do not advance until user responds]
- Close: balanced analysis with both dispositional and situational factors + which is doing the explanatory work.
[WAIT — do not advance until user responds]
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 123 lines · 125 tokens per session scan A 0d517db6513e
fundamental-attribution-error is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 7d ago), licensed MIT. It adds 125 tokens to every session and 1,937 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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