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.
npx skills add tamdogood/builder-essential-skills --skill paper-opportunity-radargit clone --depth 1 https://github.com/tamdogood/builder-essential-skillsWrote 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/tamdogood/builder-essential-skills/paper-opportunity-radar)<a href="https://agentmods.dev/skills/tamdogood/builder-essential-skills/paper-opportunity-radar"><img src="https://agentmods.dev/badge/skills/tamdogood/builder-essential-skills/paper-opportunity-radar/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/tamdogood/builder-essential-skills/paper-opportunity-radar"><img src="https://agentmods.dev/badge/skills/tamdogood/builder-essential-skills/paper-opportunity-radar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 104 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
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 | $0.00098 | $0.02587 |
| Opus 5 | $0.00049 | $0.01293 |
| Sonnet 5 | $0.00020 | $0.00517 |
| Haiku 4.5 | $0.00010 | $0.00259 |
Grade A, and why
paper-opportunity-radar 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 13d 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 — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Opportunity Radar
Treat the literature as an evidence base to traverse over time, not a feed to summarize. Build a cumulative corpus, audit important papers at claim level, look for what happened after publication, and turn only defensible gaps into testable opportunities.
This skill runs when invoked. It does not silently create a background scheduler. If the user wants a daily cadence, preserve resumable state and give their scheduler the recurring prompt in Daily Operation. Never say monitoring is active until a recurring job actually exists.
Non-negotiable Standards
- Never claim exhaustive coverage without a bounded corpus. Define the databases, query strings, dates, languages, document types, and traversal cursor. Say "all records returned by this protocol," not "all papers ever."
- Fact-check at claim level. Every factual statement in a report needs a
fetched source. Mark interpretations as
INFERENCEand gaps asUNKNOWN. - A paper verifies what its authors reported, not that the result is true. Independent replication, convergent evidence, or real-world validation is a separate evidence layer.
- Do not infer fraud. Use
INTEGRITY CONCERNfor observable anomalies. UseRETRACTED,CORRECTED, orFORMAL MISCONDUCT FINDINGonly when the publisher, institution, court, or regulator supports that status. - Citation count is attention, not validity. Peer review, venue prestige, author reputation, and code availability are signals to inspect, never proof.
- Absence of search results is not proof of novelty. Report exactly where,
how, and when prior art was searched and use
NOT FOUND IN SEARCH. - Keep four judgments separate: evidence strength, unexploredness confidence, implementation feasibility, and real-world value. Never average them into one score that hides a fatal weakness.
Inputs and Defaults
Require a topic. Infer the remaining inputs when safe:
- decision: explore a business, find a project, understand feasibility, or monitor scientific progress;
- historical horizon: earliest searchable record through today by default;
- domains and adjacent fields;
- geography, language, and publication-type limits;
- available skills, capital, equipment, compute, data, and time;
- daily depth:
standardby default;briefordeepwhen requested.
What ships with it
5 files 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.
- 13d ago First seen · 263 lines · 98 tokens per session scan A f278ac7440ab
paper-opportunity-radar is a skill published in the GitHub repository tamdogood/builder-essential-skills (197 stars, last pushed 27d ago), licensed MIT. It adds 98 tokens to every session and 2,587 once invoked, about $0.0005 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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