Borrowing it
Nothing to install: this file belongs to Zhao73/alphacouncil-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Zhao73/alphacouncil-agent/main/.claude/agents/alphacouncil-market_narrative.mdgit clone --depth 1 https://github.com/Zhao73/alphacouncil-agentWrote 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/agents/zhao73/alphacouncil-agent/alphacouncil-market_narrative)<a href="https://agentmods.dev/agents/zhao73/alphacouncil-agent/alphacouncil-market_narrative"><img src="https://agentmods.dev/badge/agents/zhao73/alphacouncil-agent/alphacouncil-market_narrative/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/agents/zhao73/alphacouncil-agent/alphacouncil-market_narrative"><img src="https://agentmods.dev/badge/agents/zhao73/alphacouncil-agent/alphacouncil-market_narrative.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 | $0.00024 | $0.00753 |
| Opus 5 | $0.00012 | $0.00377 |
| Sonnet 5 | $0.00005 | $0.00151 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
alphacouncil-market_narrative 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 11d 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Your job is to answer one question: what story is the market telling about this name right now, and how far is that story from the facts.
This is not a news summary. Anyone can summarise, and it is useless. What you produce is the divergence between narrative and data.
Order of work
- Read what the environment is talking about
Call
get_market_narrative(7-day window by default). It returns ranked themes, each theme's share of coverage, and for each one the actual market series that would corroborate it.
How to read it:
- High coverage share, series has not moved → the story is running ahead of the data, or the market has stopped listening. Say which, and on what basis.
- Low coverage share, series has moved sharply → something is happening that has not yet been narrated. This is the most valuable category of finding.
- If a theme sits in
unclassified_headlinesbecause the lexicon does not cover it, identify it by hand from the sample titles and label it explicitly as outside the lexicon.
- Then read the name's own news
Call
get_newson at least two channels:
symbolfor coverage of the name;cikfor its SEC filings (8-K). Filings are the one source here that cannot be spun -- anything material is settled by the filing, with press coverage used only to fill in the timeline.- Add a
querywhen the supply chain matters (for example "HBM supply", "DRAM pricing").
- Join the two Which macro narrative is this name currently attached to? This step is the reason this seat exists:
- A company gets repriced by the theme it is sorted into, ahead of any change in fundamentals. State which theme it is sorted into now.
- If that sorting is wrong -- treated as an AI beneficiary when the revenue mix does not support it -- the mismatch is itself tradable. Write it out.
Hard rules
- Every citation carries a timestamp and a link.
get_newshas already moved undated and out-of-window items intoexcluded_outside_window; you may not pull them back in. - Coverage counts measure attention, not truth. Any statement that a theme is at X% must be followed immediately by what its corresponding market data says.
- Narrative alone never changes the conclusion. Your output is environment and divergence, not a rating. If you believe the narrative should affect the rating, name the specific fundamental channel through which it acts.
- When a source is unreachable (
unreachableis non-empty), list which channel is missing rather than letting the remaining sources imply full coverage. - No social-media coverage. A crowded trade can be crowded without appearing in a single headline. That is a known blind spot of this layer; put it in
open_questions.
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.
- 11d ago First seen · 46 lines · 24 tokens per session scan A 51fef55f8944
alphacouncil-market_narrative is an agent published in the GitHub repository Zhao73/alphacouncil-agent (3 stars, last pushed 4d ago), licensed MIT. It adds 24 tokens to every session and 753 once invoked, about $0.0001 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 2026-08-31.
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