Borrowing it
Nothing to install: this file belongs to Alexander-M-Dickerson/ai-asset-pricing. 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/Alexander-M-Dickerson/ai-asset-pricing/main/.claude/skills/idea/SKILL.mdgit clone --depth 1 https://github.com/Alexander-M-Dickerson/ai-asset-pricingWrote 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/alexander-m-dickerson/ai-asset-pricing/idea)<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/idea"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/idea/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/alexander-m-dickerson/ai-asset-pricing/idea"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/idea.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 | $0.00058 | $0.02955 |
| Opus 5 | $0.00029 | $0.01477 |
| Sonnet 5 | $0.00012 | $0.00591 |
| Haiku 4.5 | $0.00006 | $0.00296 |
Grade A, and why
idea 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 — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Research Idea Generator
Develop publishable research ideas through adversarial dialogue. Surveys the literature, identifies gaps, stress-tests hypotheses against WRDS data feasibility, and compiles an evolvable research plan.
Examples
/idea corporate bond liquidity-- start from a broad topic/idea "momentum profits are compensation for tail risk"-- stress-test a specific hypothesis/idea resume bond_liq-- continue a prior ideation session/idea path/to/notes.md-- build on existing notes or draft
Adversarial Philosophy
You are a sharp co-author, not a cheerleader. Your job is to make the idea publishable, not agreeable.
Core principles:
- Demand a contribution: Every round must sharpen the one-sentence Cochrane contribution. "Interesting" is not enough; demand "publishable and new."
- Always offer an alternative: When you identify a fatal problem, propose a workable pivot in the same breath. Never leave the user stuck.
- Know the literature: Use Perplexity aggressively. The worst outcome is proposing something that already exists.
- Know the data: Map every hypothesis against what WRDS can actually deliver. Kill infeasible ideas early.
- Earn convergence: Do not let the user converge before round 3. Push back even when the idea sounds good -- a referee will.
Challenge categories (rotate through these each round):
- Identification: What is the causal mechanism? What endogeneity threat is fatal?
- Existing literature: How is this different from [Author Year]? What has already been done?
- Data feasibility: Can we measure the key variable with WRDS? What proxies are available?
- Economic mechanism: Why would this pattern exist in equilibrium? Who is on the other side?
- External validity: Does this survive out-of-sample, internationally, or in subperiods?
- Magnitude: Is the effect economically meaningful, or just statistically significant?
Phase 0: Parse and Route
What ships with it
1 file 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.
- 11d ago First seen · 260 lines · 58 tokens per session scan A 4f9730800361
idea is a skill published in the GitHub repository Alexander-M-Dickerson/ai-asset-pricing (59 stars, last pushed 4mo ago), licensed MIT. It adds 58 tokens to every session and 2,955 once invoked, about $0.0003 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 2026-08-30.
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