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 pareto-principlegit 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/pareto-principle)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/pareto-principle"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/pareto-principle/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/pareto-principle"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/pareto-principle.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 92 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00107 | $0.02146 |
| Opus 5 | $0.00053 | $0.01073 |
| Sonnet 5 | $0.00021 | $0.00429 |
| Haiku 4.5 | $0.00011 | $0.00215 |
Grade A, and why
pareto-principle 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The Pareto Principle (80/20)
Overview
In most real systems, a small fraction of inputs produces the majority of outputs. The pattern — heavy-tailed distribution where the vital few dominate the trivial many — is empirically robust across operations, software, and revenue. Pareto (1896) documented the distribution; Juran (1951) coined "vital few and trivial many." Key hazard: different outputs have different vital fews, and asserting "80/20" without measuring is folk reasoning.
Compose: first-principles to identify what outcome you are driving; aarrr-pirate-metrics to instrument which inputs produce which outputs; probabilistic-thinking to test the split is real and not a small-sample artifact.
When to Use
Use: team treating many items as equally important; resources spread thin; prioritization needed; you suspect a heavy-tailed distribution that hasn't been measured; deciding where to concentrate AI capex / AI adoption effort when most pilots stall and a few use cases capture the value (which AI bets to fund vs. cut against AI-native competition).
When NOT: only a few items total; safety-critical or long-tail-strategic items where the residual matters; the split is trivially obvious; using it to abandon a strategically valuable long tail.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has data and wants vital few identified — run The Process directly.
- Coach mode: vague situation or signals unfamiliarity — 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: in most systems, ~20% of inputs produce ~80% of outputs — Pareto identifies those inputs so effort goes where it matters, not spread equally.
- Check fit against When to Use / When NOT to use. Tiny dataset or safety-critical → redirect.
- Elicit the one metric they want to grow (revenue, crashes-eliminated, support tickets). The 80/20 of customer count is not the 80/20 of revenue.
[WAIT — do not advance until user responds]
- Run The Process one step at a time with their input: define output → collect distribution → rank → identify elbow → check ratio → decide on trivial many.
[WAIT — do not advance until user responds]
- Close by naming the vital few they uncovered AND their explicit decision about the trivial many (cut / maintain / invest-strategic).
[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 · 123 lines · 107 tokens per session scan A 40a2326702f1
pareto-principle is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 107 tokens to every session and 2,146 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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