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 halo-effectgit 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/halo-effect)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/halo-effect"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/halo-effect/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/halo-effect"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/halo-effect.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 | $0.00131 | $0.01792 |
| Opus 5 | $0.00066 | $0.00896 |
| Sonnet 5 | $0.00026 | $0.00358 |
| Haiku 4.5 | $0.00013 | $0.00179 |
Grade A, and why
halo-effect 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 9d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Halo Effect
Overview
A single positive or negative impression biases judgments of all unrelated attributes. A "great" CEO is assumed to have great strategy, vision, and execution; a beloved brand's features are rated higher than equivalent features from less-loved brands. Documented by Thorndike (1920), formalized by Nisbett & Wilson (1977), applied to business analysis by Rosenzweig (2007) — who showed business books overclaim because their descriptions follow company performance, not underlying reality.
Composes with fundamental-attribution-error, narrative-fallacy, confirmation-bias, hindsight-bias, survivorship-bias.
When to Use
- Reading business books, case studies, or analyst reports
- Conducting or designing performance reviews
- Conducting or designing hiring interviews
- Evaluating vendor, supplier, or partner performance
- Evaluating investment opportunities or CEO impact
- Conducting self-assessment
- Someone says "halo effect," "visionary leader," "everything they do is great"
- An "AI company" label, a marquee investor, or a famous-lab pedigree is doing the rating's work — evaluating an AI vendor, an AI-boom valuation, or a fluent model answer rated as accurate because it sounds confident
Not when: the global impression is itself the relevant judgment; attribute-by-attribute analysis would produce decision paralysis.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a specific evaluation → 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: when one impression colors all judgments of unrelated attributes, you're feeling, not evaluating.
- Check fit: judging multiple attributes of one target? Halo is a risk.
- Elicit the case: what attributes are rated? All similarly positive or negative?
[WAIT — do not advance until user responds]
- Probe: what's the global impression? Would rating each attribute independently change anything? What would a contrarian say?
[WAIT — do not advance until user responds]
- Close: name which attributes have real evidence vs. halo; propose structured rubric for next evaluation.
[WAIT — do not advance until user responds]
What ships with it
3 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.
- 9d ago First seen · 125 lines · 131 tokens per session scan A 522adaacdbb6
halo-effect is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 131 tokens to every session and 1,792 once invoked, about $0.0007 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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