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 herzberg-two-factorgit 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/herzberg-two-factor)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/herzberg-two-factor"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/herzberg-two-factor/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/herzberg-two-factor"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/herzberg-two-factor.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 | $0.00108 | $0.02192 |
| Opus 5 | $0.00054 | $0.01096 |
| Sonnet 5 | $0.00022 | $0.00438 |
| Haiku 4.5 | $0.00011 | $0.00219 |
Grade A, and why
herzberg-two-factor 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 8d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Herzberg Two-Factor Theory
Overview
Herzberg's 1959 Pittsburgh study found satisfaction and dissatisfaction are two independent axes. Hygiene factors (salary, working conditions, job security) only remove dissatisfaction — never create motivation. Motivators (achievement, recognition, responsibility, growth, the work itself) create genuine engagement. More hygiene spending never produces motivation; only job enrichment — redesigning work for higher responsibility and autonomy — does.
Composition with neighbors: Use principal-agent when incentive structures keep failing (designers mistake hygiene for motivators). Use nudge-theory after Herzberg to design low-friction paths to the motivating actions identified.
When to Use
- Team shows adequate performance but low energy — people meet the minimum but go no further
- Talent leaving for "better opportunities" and exit interviews are uninformative
- Compensation recently increased but morale didn't improve, or reset within months
- Designing compensation structure, job role, or performance review system
- Bidding wars for scarce AI/ML talent — matched counter-offers still lose people; AI-capex-driven pay bands keep rising but engagement doesn't follow (an AI-native competitor is out-motivating, not just out-paying, you)
- Someone says: "motivation," "engagement," "retention," "why isn't the team energized"
When NOT to use: Clear hygiene gap exists (salary below market, unsafe conditions) — fix those first. Skill gap, not motivation gap. Very short time horizon (72-hour sprint).
Coaching Novices (Adaptive Front Door)
- Engine mode: specific team/role described → run The Process directly.
- Coach mode: unfamiliar or 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 what-it-is: salary/benefits can only remove unhappiness — only challenging work, real responsibility, and recognition for achievement create genuine motivation.
- Check fit against When to Use / When NOT to use. If clear hygiene gap exists, redirect to fixing hygiene first.
- Elicit their real case — "people aren't motivated" is not a case; get the specific behavioral signal.
[WAIT — do not advance until user responds]
- Classify each complaint/satisfaction item as hygiene or motivator one at a time before prescribing.
[WAIT — do not advance until user responds]
- Close: "The reason raising salary didn't fix this is ___. The one motivator intervention most likely to move engagement here is ___."
[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.
- 8d ago First seen · 124 lines · 108 tokens per session scan A e0e8867a7a92
herzberg-two-factor is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 108 tokens to every session and 2,192 once invoked, about $0.0005 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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