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 juliakorovsky/claude-skills --skill trace-paper-followupsgit clone --depth 1 https://github.com/juliakorovsky/claude-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/juliakorovsky/claude-skills/trace-paper-followups)<a href="https://agentmods.dev/skills/juliakorovsky/claude-skills/trace-paper-followups"><img src="https://agentmods.dev/badge/skills/juliakorovsky/claude-skills/trace-paper-followups/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/juliakorovsky/claude-skills/trace-paper-followups"><img src="https://agentmods.dev/badge/skills/juliakorovsky/claude-skills/trace-paper-followups.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 | $0.00184 | $0.03546 |
| Opus 5 | $0.00092 | $0.01773 |
| Sonnet 5 | $0.00037 | $0.00709 |
| Haiku 4.5 | $0.00018 | $0.00355 |
Grade A, and why
trace-paper-followups scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- `urllib` over a misconfigured cert store throws `CERTIFICATE_VERIFY_FAILED`; the How it starts
The opening of the file, as written. The whole thing — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trace what later research did with a paper
The job: start from one seed work (a paper, model, method, or architecture) and produce an honest map of the research that came after it — separating real follow-ups (things that extend or improve the seed) from the large mass of papers that only cite it in passing.
This is a pipeline of cheap automated steps wrapped around one irreducibly model-driven step: reading and judging each candidate. Citation databases and keyword filters are noisy; the value you add is reading abstracts and deciding what each paper actually did relative to the seed. Don't try to automate that judgment away.
The pipeline
Work through these in order. Each script lives in scripts/ and is run with
python3. Keep every stage's output in separate files so the lineage is
traceable (raw citations → judged → prose), and pick a short <slug> for the
seed (e.g. sam, mamba).
1. Identify the seed and read it first
Get the seed's arXiv id (or DOI / Semantic Scholar id). If you'll be making
claims about what the seed is, read the seed paper before trusting how other
papers describe it — secondhand summaries drift, and a comparison built on a
wrong mental model of the seed is wrong everywhere. Download it with
download_pdfs.py --ids <seed-id> and digest it with digest.py.
Then write a small <slug>_seed.json profile of the seed — its name, any
aliases it's known by, arxiv id, a one-line what it is, and its baseline
capabilities/modalities. This is what later questions are answered against:
"did anyone add video to Moshi?" can only be answered honestly if the record says
Moshi has no video to begin with. Shape:
{ "slug": "moshi", "name": "Moshi", "aliases": ["Moshi", "Mimi"],
"arxiv": "2410.00037", "what": "Full-duplex speech-text model for real-time dialogue.",
"capabilities": ["speech-in", "speech-out", "full-duplex", "real-time"],
"modalities": ["audio", "text"],
"code": "https://github.com/kyutai-labs/moshi" }
Include the seed's own reference implementation (code) if it has one — set it to
null if there genuinely isn't one.
What ships with it
9 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.
- evals/evals.json 1.6 KB
- examples/dmel-followups-report.md 5.8 KB
- README.md 4.4 KB
- references/example_run.md 3.4 KB
- scripts/digest.py 5.0 KB runs code
- scripts/download_pdfs.py 7.2 KB runs code
- scripts/fetch_citations.py 7.8 KB runs code
- scripts/manifest.py 3.8 KB runs code
- scripts/verify_links.py 3.5 KB runs code
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.
- 12d ago First seen · 244 lines · 184 tokens per session scan A b1f7cb6849df
trace-paper-followups is a skill published in the GitHub repository juliakorovsky/claude-skills (2 stars, last pushed 21d ago), licensed MIT. It adds 184 tokens to every session and 3,546 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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