paper-opportunity-radar

paper-opportunity-radar is a skill for Codex from tamdogood/builder-essential-skills. It costs 98 tokens per session (2,587 once invoked), scanned A, original, MIT.

A research workflow for repeatedly reviewing papers on a chosen topic and turning well-supported gaps into possible projects or business ideas. It checks claims, methods, integrity signals, and independent evidence.

In plain words
What is it for?
Use it for daily or retrospective paper reviews, claim-level source checking, cumulative literature searches, and identifying feasible opportunities from research gaps.
Why use it?
It prevents a paper list from becoming an unsupported summary by separating what authors reported from what later evidence confirms.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: $skill-name invocation.

Good fit Use it for daily or retrospective paper reviews, claim-level source checking, cumulative literature searches, and identifying feasible opportunities from research gaps.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tamdogood/builder-essential-skills/paper-opportunity-radar
Install

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.

Any agent
npx skills add tamdogood/builder-essential-skills --skill paper-opportunity-radar
Clone the repo
git clone --depth 1 https://github.com/tamdogood/builder-essential-skills

Made for: Codex.

Wrote 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.

agentmods badge for paper-opportunity-radar

README.md
[![agentmods](https://agentmods.dev/badge/skills/tamdogood/builder-essential-skills/paper-opportunity-radar/github.svg)](https://agentmods.dev/skills/tamdogood/builder-essential-skills/paper-opportunity-radar)
Your own site
<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.

agentmods 80×15 button for paper-opportunity-radar

Your own site · 80×15
<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>
Per session 98 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,587 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
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.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 13d ago against content hash f278ac7440ab, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

skills/paper-opportunity-radar/SKILL.md · 263 lines

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 INFERENCE and gaps as UNKNOWN.
  • 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 CONCERN for observable anomalies. Use RETRACTED, CORRECTED, or FORMAL MISCONDUCT FINDING only 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: standard by default; brief or deep when requested.

Read the full file on GitHub · 263 lines

Files

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.

Changes

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.

  1. 13d ago First seen · 263 lines · 98 tokens per session scan A f278ac7440ab

Subscribe to this mod's changes

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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