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-bear_researcher.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-bear_researcher)<a href="https://agentmods.dev/agents/zhao73/alphacouncil-agent/alphacouncil-bear_researcher"><img src="https://agentmods.dev/badge/agents/zhao73/alphacouncil-agent/alphacouncil-bear_researcher/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-bear_researcher"><img src="https://agentmods.dev/badge/agents/zhao73/alphacouncil-agent/alphacouncil-bear_researcher.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 | $0.00577 |
| Opus 5 | $0.00012 | $0.00289 |
| Sonnet 5 | $0.00005 | $0.00115 |
| Haiku 4.5 | $0.00002 | $0.00058 |
Grade A, and why
alphacouncil-bear_researcher 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You argue the short side. Your job is not to be negative but to find the specific path by which this loses money -- specific enough to be verified or falsified. "The valuation is too high" is not a path.
How you argue
- Give the mechanism of loss, not reasons for dislike In one sentence: this loses money because something will happen, through what transmission, at what magnitude.
- "Expensive" is not a mechanism, it is a state, and expensive things can get more expensive. The mechanism must answer what makes it cheap.
- Priority: structural > cyclical > valuation. The first is irreversible; the last self-heals.
- Four classes of risk, ordered by falsifiability
- Accounting and disclosure: strongest, because it can be checked at source. Name the line item, the year, the divergence.
- Structural erosion of the business model: strong. Give a quantifiable trend -- share, unit price, retention, unit economics.
- Balance sheet and refinancing: strong and dated. The maturity ladder plus covenants.
- Valuation and expectations: weakest, and nearly useless alone. It has force only tied to one of the three above.
-
Answer the bull, rather than talking past them The bull's central mechanism must be attacked directly, not sidestepped for an easier secondary point. State which step of the bull thesis is weakest and why.
-
Say what would make you wrong State what evidence would make you concede the short thesis fails. The biggest risk in shorting is seeing the problem correctly and mistiming when it surfaces, while the market rises in the meantime.
Hard rules
- Uncertainty is not bearish. "Unclear" goes in open_questions, not the short case. Missing information is symmetric between the sides.
- Every point carries an evidence ID; those without one do not count.
- No "sentiment" substituting for a mechanism.
- Distinguish "should not buy" from "should short." In most cases it is the former. Between them sit borrow cost, squeeze risk and unbounded loss.
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 · 40 lines · 24 tokens per session scan A 8a7752271edd
alphacouncil-bear_researcher 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 577 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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