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 tokenbender/agent-guides --skill capability-horizon-estimatorgit clone --depth 1 https://github.com/tokenbender/agent-guidesWrote 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/tokenbender/agent-guides/capability-horizon-estimator)<a href="https://agentmods.dev/skills/tokenbender/agent-guides/capability-horizon-estimator"><img src="https://agentmods.dev/badge/skills/tokenbender/agent-guides/capability-horizon-estimator/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/tokenbender/agent-guides/capability-horizon-estimator"><img src="https://agentmods.dev/badge/skills/tokenbender/agent-guides/capability-horizon-estimator.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 | $0.00084 | $0.01783 |
| Opus 5.5 | $0.00034 | $0.00713 |
| Sonnet 5.5 | $0.00017 | $0.00357 |
| Haiku 4.5 | $0.00008 | $0.00178 |
Grade A, and why
capability-horizon-estimator 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 1mo 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capability & Time Horizon Estimator
Estimate what a model can do and how long it takes using the METR time-horizon framework: every task has a human-equivalent duration $t$, every model has a 50% time horizon $h_{50}$, and success probability follows a logistic curve in $\log(h/t)$.
When to use
- "Can you do X autonomously?" — give a calibrated yes/maybe/no instead of vibes
- "How long will this agent run take?" — wall-clock estimate with retry overhead
- Scoping: should this be one task, or decomposed?
- Deciding attempt budget (best-of-N) for a stretch task
The core model
Step 1 — Estimate human-equivalent task time $t$ (minutes).
Anchor against known reference points (full table in references/benchmark-catalog.md):
| Reference task | Human time |
|---|---|
| SWE-bench Verified issue | 7 min – 2 h |
| HCAST task bands | 15 min / 1 h / 4 h / 8 h |
| RE-Bench ML research task | 8 h |
| Small bug fix, clear repro | 15–60 min |
| Multi-file feature | 2–8 h |
| Cross-repo refactor | 4–16 h |
| Kaggle competition (MLE-bench) | days of human effort |
| FrontierMath T4 problem | days–weeks of expert time |
Estimate for a low-context professional (new hire, contractor), not the resident expert — that is what the horizons are calibrated against.
Step 2 — Get the model's horizon $h_{50}$ (minutes).
Look it up in references/horizon-data.md (METR TH v1.1, May 2026). If the model isn't listed, extrapolate from release date:
$$h_{50}(\text{date}) = h_{50}(\text{ref}) \times 2^{(\text{date} - \text{ref}) / D}, \quad D \approx 130\text{–}190 \text{ days}$$
Use $D = 150$ days as default; state the range. Post-2024 data supports faster ($\sim$90–130 days); all-time average is $\sim$190.
Step 3 — Success probability.
$$p = \sigma!\big(\beta \cdot \ln(h_{50}/t)\big), \quad \beta \approx 0.8 \text{ (range 0.6–0.9)}, \quad \sigma(x) = \frac{1}{1+e^{-x}}$$
Sanity anchors with $\beta = 0.8$: $t = h_{50} \Rightarrow p = 50%$ · $t = h_{50}/5.7 \Rightarrow p = 80%$ · $t = 2h_{50} \Rightarrow p \approx 36%$ · $t = 4h_{50} \Rightarrow p \approx 25%$.
What ships with it
4 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.
- 1mo ago First seen · 102 lines · 84 tokens per session scan A dfc79b572f0b
capability-horizon-estimator is a skill published in the GitHub repository tokenbender/agent-guides (367 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 84 tokens to every session and 1,783 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
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