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 multi-stage-cascade-extractiongit 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/multi-stage-cascade-extraction)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/multi-stage-cascade-extraction"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/multi-stage-cascade-extraction/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/multi-stage-cascade-extraction"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/multi-stage-cascade-extraction.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 | $0.00116 | $0.00561 |
| Opus 5 | $0.00058 | $0.00280 |
| Sonnet 5 | $0.00023 | $0.00112 |
| Haiku 4.5 | $0.00012 | $0.00056 |
Grade A, and why
multi-stage-cascade-extraction 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 11d 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.
What it actually says
Multi-Stage Cascade Extraction
Mention detection → coreference clustering → [saliency] → relation extraction, all stages consuming the full prior stage's output. Covers SciERC/SciREX/NLP-Contribution-Graph — three methods with different stage counts but the same "consume-the-full-prior-layer" structure (graph correction S6: merged under the unifying rule "same action-sequence length → mergeable via parameterization").
Execution
Subagent — spawned via spawn-agent skill.
Why Direct From paper-fetch, Not Through unit-segmentation
This cascade discovers its own mention spans over the whole document rather than consuming pre-segmented sentence/clause units — sentence-level segmentation is the wrong granularity for a method whose relations are 99% cross-sentence (SciREX's own reported figure). This is a deliberate graph choice, not an oversight — see spec §5's flagged note before "fixing" this dependency.
Errors Compound Stage-Over-Stage
NLP Contribution Graph's own reported consistency figures fall from stage to stage (67.92% → 41.82% → 22.31%) — this is the shared risk profile of this whole method family, not specific to one method. Producing every stage's intermediate output (not just the final relations) is what makes this compounding visible and debuggable.
Available SOPs
| SOP | When to use |
|---|---|
| spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
What ships with it
2 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.
- 11d ago First seen · 42 lines · 116 tokens per session scan A dac09a83d47e
multi-stage-cascade-extraction is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (449 stars, last pushed yesterday), licensed Apache-2.0. It adds 116 tokens to every session and 561 once invoked, about $0.0006 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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