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 yogsoth-ai/de-anthropocentric-research-engine --skill paper-fetchgit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWrote 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/yogsoth-ai/de-anthropocentric-research-engine/paper-fetch)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/paper-fetch"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/paper-fetch/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/yogsoth-ai/de-anthropocentric-research-engine/paper-fetch"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/paper-fetch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00182 | $0.01651 |
| Opus 5 | $0.00091 | $0.00826 |
| Sonnet 5 | $0.00036 | $0.00330 |
| Haiku 4.5 | $0.00018 | $0.00165 |
Grade A, and why
paper-fetch 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Fetch
If the caller already has the paper — a local .md/.txt/.pdf path, or a direct
HTTP(S) PDF URL (path ends in .pdf, ignoring query and fragment) — read it
directly before considering any search route. Record source_channel as
local_file, local_pdf, or direct_pdf. If the read fails, return not_found
and do not fall back to alphaxiv, Semantic Scholar, bioRxiv, or medRxiv.
The pipeline's sole entry point: retrieves a paper and lands it on disk. It checks
the cache first, reads already-identified sources (local files, direct PDF URLs)
with no search at all, and otherwise uses the fixed fallback (alphaxiv → Semantic
Scholar routing → bioRxiv/medRxiv → not_found).
Decoupled from literature-engine's literature-research/literature-search/
literature-overview — this SOP holds its own retrieval calls rather than delegating.
Why an already-supplied file still enters through this SOP
Skipping straight to a reading SOP with the caller's own path looks like it saves
a step, but 13 downstream SOPs take meta_path and read only section line ranges
(star-awarding reads method + results; first-pass-skim's "headings only, never
bodies" constraint holds because it is handed shallow ranges). A bare path
carries no index, and a bare .pdf carries no extracted text at all.
So the thing to skip is the four-channel search, not the landing and indexing.
That is what Step 1 does: no network lookup, same landing step, same output
contract. Tactics keep passing paper_ref through unchanged and never learn there
was a new input form.
Landed layout
context/papers/<timestamp>-<title-slug>/
source.md the paper, as fetched
source.meta.json metadata + line-number section index
All landed filenames are lowercase. <title-slug> is lowercased, non-alphanumerics collapsed to hyphens, Windows-illegal characters (: * ? " < > |) stripped, truncated to 60 chars against path-length limits.
Execution
Subagent — spawned via spawn-agent skill.
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.
- 13d ago First seen · 100 lines · 182 tokens per session scan A aa49f7db8804
paper-fetch is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (462 stars, last pushed 3d ago), licensed Apache-2.0. It adds 182 tokens to every session and 1,651 once invoked, about $0.0009 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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