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 classification-navigationgit 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/classification-navigation)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/classification-navigation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/classification-navigation/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/classification-navigation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/classification-navigation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Prompt Injection · line 62 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable 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 | $0.00015 | $0.00590 |
| Opus 5 | $0.00008 | $0.00295 |
| Sonnet 5 | $0.00003 | $0.00118 |
| Haiku 4.5 | $0.00002 | $0.00059 |
Grade A, and why
classification-navigation 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 8d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Classification Navigation
Navigates the IPC/CPC patent classification hierarchy to discover patents through structured taxonomy traversal — drilling down into subclasses and expanding laterally to related classes.
Stages
1. Top-Level Classification Identification
- Determine relevant IPC/CPC sections and classes from seed patents or technology description
- Map the technology domain to classification entry points
- Identify primary and secondary classification codes
2. Subclass Drill-Down
- For each identified class, drill into subclass, main group, and subgroup levels
- Assess patent density at each level
- Identify the most specific relevant classification codes
3. Lateral Expansion to Related Classes
- From identified subclasses, explore sibling and cousin classes
- Follow "see also" and "related" cross-references in classification schemes
- Identify adjacent technology areas that may contain relevant patents
4. Cross-Reference Validation
- Verify that lateral expansion yields relevant results
- Check that discovered classes actually contain patents related to the target technology
- Prune irrelevant branches and confirm final classification scope
Available SOPs
| SOP | Role |
|---|---|
| patent-query-formulation | Generate IPC/CPC-based search queries |
| patent-categorization | Validate classification relevance of found patents |
| saturation-detection | Determine when classification space is fully explored |
Execution Guidance
- Start broad (section/class level) and narrow down based on relevance
- IPC and CPC diverge at subgroup level — check both schemes
- Pay attention to classification reform dates — codes may have changed
- Cross-references in CPC scheme definitions reveal non-obvious connections
- A single invention often spans 3-5 classification codes — always check secondary classifications
Yield Report
Report to calling strategy after each execution:
- Classification codes explored (with depth level)
- New relevant codes discovered
- Patent count per classification code
- Coverage assessment (% of relevant classification space explored)
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.
- 8d ago First seen · 75 lines · 15 tokens per session scan A 6a778feed67a
classification-navigation is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (456 stars, last pushed yesterday), licensed Apache-2.0. It adds 15 tokens to every session and 590 once invoked, about $0.0001 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 2026-09-03.
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patent-search
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patent-application
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patent-disclosure
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Deep Research
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omnisci
Run OmniScientist end to end in the OmniScientist CLI using DeepSeek V4 Flash. Turn raw research data (images, signals, audio, video, 3-D, tables, or graphs) and an open direction into perceived evidence, a falsifiable hypothesis, recorded analysis, real citations, a gated candidate paper, PDF, and Overleaf bundle.…