Borrowing it
Nothing to install: this file belongs to fbabelle/PrettySeriousResearcher. 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/fbabelle/PrettySeriousResearcher/main/.claude/skills/research-topic-selection/SKILL.mdgit clone --depth 1 https://github.com/fbabelle/PrettySeriousResearcherWrote 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/fbabelle/prettyseriousresearcher/research-topic-selection)<a href="https://agentmods.dev/skills/fbabelle/prettyseriousresearcher/research-topic-selection"><img src="https://agentmods.dev/badge/skills/fbabelle/prettyseriousresearcher/research-topic-selection.svg" alt="Measured on agentmods" 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 | $0.00057 | $0.02264 |
| Opus 5 | $0.00028 | $0.01132 |
| Sonnet 5 | $0.00011 | $0.00453 |
| Haiku 4.5 | $0.00006 | $0.00226 |
Grade A, and why
research-topic-selection 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 yesterday.
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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Phase 1 — Topic research, selection & skeleton
The most leveraged phase: a well-chosen, well-scoped topic with a defensible gap is worth more than any later cleverness. Output of this phase is a confirmed topic + direction, a novelty/gap claim, and a paper skeleton.
Step 1 — The two-direction interview (hard stop; do not skip)
Before any investigation, confirm scope with the user. These answers shape everything downstream — never assume them:
- Domain & sub-area (e.g. LLM agents for financial QA; portfolio optimization; time-series forecasting; market-microstructure RL; risk/factor models).
- Target problem(s) — the specific pain or open question.
- Expected outcome — a new method/system, or an analysis/understanding result?
- Improve vs challenge — patch/extend existing systems, or test/break a prevailing thesis?
- Direction (a) vs (b):
- (a) Algorithm/technical — push the field: new architecture, mechanism, or thesis (think Attention Is All You Need, ResNet, MoE). Demands deep, comprehensive investigation before any claim. Higher risk, higher novelty.
- (b) Experimental/analytical — survey, benchmark, replication, ablation/variation study, limitation analysis, or parameter refinement of existing systems. Lower invention, high rigor and interpretation.
- Venue & length target (sets the bar and the skeleton): NeurIPS/ICML/ICLR/AAAI/ACL; or finance venues — Journal of Finance, RFS, JFE, arXiv q-fin, SSRN; workshops vs full track. Fire
research-venue-selectionearly to turn this into a venue profile (rubric, acceptance bar, section/length template, disclosure/anonymity rules, deadline windows) that becomes a scored input to topic choice below — the topic is picked partly for venue fit, not retrofitted to a venue later (venue-as-input).
Record the answers as the project charter (top of architecture.md). The orchestrator will not advance past this without them.
Step 2 — Broad prior-art & novelty-gap investigation (breadth)
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.
- yesterday Changed · +1 lines 6942a26d8f98
- 6d ago First seen · 83 lines · 57 tokens per session scan A 1286e514a92d
research-topic-selection is a skill published in the GitHub repository fbabelle/PrettySeriousResearcher (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 57 tokens to every session and 2,264 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 2026-08-31.
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