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-historical-cycle-detectiongit 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-historical-cycle-detection)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-historical-cycle-detection"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-historical-cycle-detection/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-historical-cycle-detection"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-historical-cycle-detection.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 | $0.00068 | $0.01441 |
| Opus 5 | $0.00034 | $0.00720 |
| Sonnet 5 | $0.00014 | $0.00288 |
| Haiku 4.5 | $0.00007 | $0.00144 |
Grade A, and why
s4h-historical-cycle-detection 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Historical Cycle Detection
Most situations that feel unprecedented are instances of recognisable cycles. The value of cycle identification is not prediction — cycles don't produce certainties — it is orientation. Knowing where you are in the cycle tells you what phase logic to apply, what the typical incentive pressures are at this point, and what tends to come next absent a significant disrupting variable.
Your Process
Step 1: Describe the Situation and Recent Trajectory What is happening and how has it developed? Focus on direction of change — accelerating, plateauing, reversing — and the sentiment among participants. Sentiment is often the most reliable indicator of cycle position because it drives behaviour independently of fundamentals.
Framing check: Confirm the specific situation before continuing. State what you've identified — the actual subject being analyzed, the domain it belongs to, and the rough time horizon in view — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the specific situation and its trajectory]. 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: Match to the Most Fitting Cycle Evaluate against these candidate cycles. List every candidate cycle that is a plausible match, with one sentence of reasoning for each.
Before narrowing: Show the complete set of plausible candidate cycles to the user first. Use AskUserQuestion:
- Question: "I've identified the following cycles as plausible matches: [list each candidate with one sentence of reasoning]. Before I select the strongest match, 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 cycle to prioritise
- Add a missing one — user will describe an alternative cycle to consider
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 · 136 lines · 68 tokens per session scan A 8b0b392f777b
s4h-historical-cycle-detection is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 1,441 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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