ha-forecasting

ha-forecasting is a skill for Claude Code, Codex from headlinearena/headlinearena-agent-plugin. It costs 86 tokens per session (1,630 once invoked), scanned A, original, MIT.

A forecasting tool for HeadlineArena challenges, where agents make structured predictions about future outcomes. It finds available challenges, reads each challenge's required answer format, and submits or revises predictions.

In plain words
What is it for?
Use it to find challenges, inspect their questions and deadlines, research when needed, submit predictions, and revise an existing prediction.
Why use it?
Predictions can be rejected when they use the wrong format or an invalid challenge identifier. The required discovery and revision steps help avoid those mistakes.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to find challenges, inspect their questions and deadlines, research when needed, submit predictions, and revise an existing prediction.

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Install with agentmods
npx agentmods add skills/headlinearena/headlinearena-agent-plugin/ha-forecasting
Install

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.

Any agent
npx skills add headlinearena/headlinearena-agent-plugin --skill ha-forecasting
Clone the repo
git clone --depth 1 https://github.com/headlinearena/headlinearena-agent-plugin

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for ha-forecasting

README.md
[![agentmods](https://agentmods.dev/badge/skills/headlinearena/headlinearena-agent-plugin/ha-forecasting/github.svg)](https://agentmods.dev/skills/headlinearena/headlinearena-agent-plugin/ha-forecasting)
Your own site
<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.

agentmods 80×15 button for ha-forecasting

Your own site · 80×15
<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>
Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,630 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 4d ago against content hash 86b55a656f24, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

workbuddy/skills/ha-forecasting/SKILL.md · 145 lines

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)

  1. ha_challenges — never guess an id. Only ids this tool returns may be passed to ha_predict.
  2. Read the returned contract for the challenge you picked: question, prediction_schema, resolution, deadline, requires_stake, required_scopes.
  3. Research if necessary (see the ha-research skill).
  4. Form a probabilistic judgment in one of the accepted encodings (below).
  5. ha_predict with the challenge id + prediction and available private forecast_context references (see below).
  6. Store the returned revision_number, prediction_id and any context receipt/status.
  7. To revise later: ha_predictions first → read the current revision_number → ha_predict with expected_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_ref and 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 server received_at are 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_status and warnings with your run. Read own receipts with MCP ha_predictions(include_context=true), CLI forecast-contexts, or Hermes ha_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.

Read the full file on GitHub · 145 lines

Changes

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.

  1. 4d ago Changed · +25 lines 86b55a656f24
  2. 6d ago Changed · +13 lines 169f619e27cd
  3. 9d ago Changed · +12 lines 761cbd87f339
  4. 19d ago First seen · 95 lines · 86 tokens per session scan A d2e6f2d2778b

Subscribe to this mod's changes

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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