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-temporal-horizon-mappinggit 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-temporal-horizon-mapping)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-temporal-horizon-mapping"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-temporal-horizon-mapping/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-temporal-horizon-mapping"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-temporal-horizon-mapping.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 | $0.00066 | $0.01162 |
| Opus 5 | $0.00033 | $0.00581 |
| Sonnet 5 | $0.00013 | $0.00232 |
| Haiku 4.5 | $0.00007 | $0.00116 |
Grade A, and why
s4h-temporal-horizon-mapping 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.
Temporal Horizon Mapping
Decisions that look good now often look very different at 1, 3, or 10 years. The most consequential errors in judgment come not from bad reasoning in the moment but from evaluating a decision at the wrong time horizon — optimizing for the immediate while the real costs land later. Making all three horizons explicit forces the tradeoff into view rather than leaving it implicit.
Your Process
Step 1: State the Decision Name the decision being evaluated and the current context in which it is being made. Clarity here prevents analysis drifting to adjacent decisions.
Framing check: Confirm the specific decision before continuing. State what you've identified — the actual decision being evaluated and the context in which it is being made — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the specific decision and its context]. 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: Map Immediate Consequences (0–3 months) What is the likely state immediately after acting? What resources are committed or freed? Who is affected and how? What has this enabled or closed off in the near term?
Step 3: Map Medium-Term Consequences (6–24 months) What does the situation look like after the initial effects have compounded? What second-order effects emerge? Who gains or loses standing? What dependencies or path-dependencies have formed?
Step 4: Map Long-Term Consequences (3+ years) What has the decision made likely or unlikely at scale and over time? What is the structural change — to capabilities, relationships, markets, culture? What would be very difficult to reverse by this point?
Step 5: Flag Reversals Identify decisions that look positive short-term but create long-term problems — and the reverse. These reversals are the highest-value output of this analysis.
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 · 66 tokens per session scan A 9afc412d4bac
s4h-temporal-horizon-mapping is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 1,162 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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