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 human-avatar/skills-for-humanity --skill s4h-systems-leverage-analysisgit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-systems-leverage-analysis)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-systems-leverage-analysis"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-systems-leverage-analysis/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/human-avatar/skills-for-humanity/s4h-systems-leverage-analysis"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-systems-leverage-analysis.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 | $0.00054 | $0.01069 |
| Opus 5 | $0.00027 | $0.00535 |
| Sonnet 5 | $0.00011 | $0.00214 |
| Haiku 4.5 | $0.00005 | $0.00107 |
Grade A, and why
s4h-systems-leverage-analysis 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systems Leverage Analysis
Most interventions target low-leverage parameters — adjusting numbers, tweaking rates — when high-leverage structural points are available and being ignored. Donella Meadows identified 12 places to intervene in a system, ranging from parameters (nearly powerless) to paradigm (most powerful). The reason high-leverage points go unused is that they face the highest resistance; understanding this is part of the analysis.
Your Process
Step 1: List Candidate Interventions Gather all interventions currently being considered or tried. Include past attempts that failed.
Framing check: Confirm the specific system and the interventions in focus before continuing. State what you've identified — the system being analysed and the intervention set you're working with — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the system and the interventions being examined]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
Step 2: Classify by Meadows Hierarchy Map each intervention to its leverage level:
- Low: numbers/parameters, buffer sizes, flow rates
- Medium: feedback loop strength, information flows, rules of the system
- High: goals of the system, system structure, paradigm (the beliefs that create the system)
Step 3: Identify the Default Level What level is typically targeted — and why? Understand the political, cognitive, or practical reasons low-leverage points get chosen.
Step 4: Surface Ignored High-Leverage Points
Before narrowing: Show the complete classified set of interventions from Step 2 to the user first. Use AskUserQuestion:
- Question: "I've identified [N] interventions across the leverage hierarchy. Before I select the highest-leverage options to focus on, are there any you'd flag as especially important, or any I've missed?"
- Header: "Prioritise"
- Options:
- Proceed with your selection — the set looks right
- Flag one — user will name a specific intervention to include
- Add a missing one — user will describe it
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 · 97 lines · 54 tokens per session scan A a1dbccb4d732
s4h-systems-leverage-analysis is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 1,069 once invoked, about $0.0003 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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