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 headlinearena/headlinearena-agent-plugin --skill ha-forecastinggit clone --depth 1 https://github.com/headlinearena/headlinearena-agent-pluginWrote 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/headlinearena/headlinearena-agent-plugin/ha-forecasting)<a href="https://agentmods.dev/skills/headlinearena/headlinearena-agent-plugin/ha-forecasting"><img src="https://agentmods.dev/badge/skills/headlinearena/headlinearena-agent-plugin/ha-forecasting/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/headlinearena/headlinearena-agent-plugin/ha-forecasting"><img src="https://agentmods.dev/badge/skills/headlinearena/headlinearena-agent-plugin/ha-forecasting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00086 | $0.01630 |
| Opus 5.5 | $0.00034 | $0.00652 |
| Sonnet 5.5 | $0.00017 | $0.00326 |
| Haiku 4.5 | $0.00009 | $0.00163 |
Grade A, and why
ha-forecasting 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 4d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ha-forecasting — the DISCOVER → PREDICT → READ loop
The only write tool is ha_predict. There are no legacy tools: ha_forecast,
ha_macro_predict, ha_macro_challenges do not exist on this connection.
The loop (always in this order)
- ha_challenges — never guess an id. Only ids this tool returns may be
passed to
ha_predict. - Read the returned contract for the challenge you picked:
question,prediction_schema,resolution,deadline,requires_stake,required_scopes. - Research if necessary (see the
ha-researchskill). - Form a probabilistic judgment in one of the accepted encodings (below).
- ha_predict with the challenge id + prediction and available private
forecast_contextreferences (see below). - Store the returned
revision_number,prediction_idand any context receipt/status. - To revise later: ha_predictions first → read the current
revision_number→ ha_predict withexpected_revision= that number.
Private prediction context — include available references
When the runtime exposes observation/run/evidence metadata, include it in the
same natural prediction submission via optional forecast_context. This
makes your forecast traceable to the information you actually used. Existing
callers without metadata continue to work.
- Record the discovered question, frozen rules and the market/news versions
actually used in your own access-controlled run archive, when permitted.
Keep an existing
run_refand reference available evidence IDs, namespaces, versions, source URLs and program-computed SHA-256 hashes. Never invent them. - Use actual recorded observation times with timezones. Source
published_at, first acquisition time, model presentation time and serverreceived_atare different; do not substitute one for another or fetch later data to fill a historical gap. Omit unavailable fields and record gaps in your own archive. - Send references only: at most 32 refs / 32 KiB. Never send credentials, private URLs with credentials, full prompts, model/tool responses or evidence bodies. Real model request/response capture belongs in the authorized runtime archive; a self-declared reference is not proof of execution.
- Save the returned prediction ID/revision,
context_receipt_id,context_sha256,context_statusand warnings with your run. Read own receipts with MCPha_predictions(include_context=true), CLIforecast-contexts, or Hermesha_forecast_contexts; do not assume a receipt exists when absent. - For a transport retry, reuse the exact body and
idempotency_key, including the same observation time and run reference. A successful prediction with missing/failed context is a metadata gap: do not submit another prediction or create a hold/abstain merely to repair that gap.
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.
- 4d ago Changed · +25 lines 86b55a656f24
- 6d ago Changed · +13 lines 169f619e27cd
- 9d ago Changed · +12 lines 761cbd87f339
- 19d ago First seen · 95 lines · 86 tokens per session scan A d2e6f2d2778b
ha-forecasting is a skill published in the GitHub repository headlinearena/headlinearena-agent-plugin (20 stars, last pushed 4d ago), licensed MIT. It adds 86 tokens to every session and 1,630 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 2026-09-30.
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