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 benchmark-sweepgit 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/benchmark-sweep)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/benchmark-sweep"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/benchmark-sweep/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/benchmark-sweep"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/benchmark-sweep.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 59 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 | $0.00024 | $0.00664 |
| Opus 5 | $0.00012 | $0.00332 |
| Sonnet 5 | $0.00005 | $0.00133 |
| Haiku 4.5 | $0.00002 | $0.00066 |
Grade A, and why
benchmark-sweep 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 9d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchmark Sweep
Systematically scan all known solutions in a domain, catalog their properties, and identify gaps where no solution exists.
State Ledger
| Resource | Target | Current | % |
|---|---|---|---|
| web-search | 30 | 0 | 0% |
| web-research | 10 | 0 | 0% |
| paper-overview | 30 | 0 | 0% |
| paper-search | 20 | 0 | 0% |
| paper-research | 8 | 0 | 0% |
HARD-GATE
Cannot exit strategy until ≥80% of each budget line is consumed OR yield targets are met with justification for remaining budget.
Available Tactics
| Tactic | Role |
|---|---|
| coverage-analysis | Inventory → crossing → intersection evaluation pipeline |
| evaluation-filtering | Score and filter generated gap-filling ideas |
Available SOPs
| SOP | Role |
|---|---|
| benchmark-inventory | Catalog all known solutions with performance/applicability/limitations |
| method-problem-crossing | Build cross-reference matrix from inventory |
| intersection-evaluation | Annotate matrix cells as explored/partial/unexplored |
| enumeration-synthesis | Synthesize sweep findings into structured report |
Execution Guidance
- Inventory: Run benchmark-inventory to catalog all known methods
- Structure: Use method-problem-crossing to organize into matrix form
- Evaluate: Run intersection-evaluation to find gaps
- Generate: For each gap, brainstorm potential solutions
- Filter: Apply evaluation-filtering to rank gap-filling ideas
- Synthesize: Produce final report via enumeration-synthesis
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| coverage-analysis | Systematic coverage evaluation pipeline — benchmark inventory, method-problem crossing, and intersection evaluation to map explored vs unexplored solution space. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| creative-ideation-benchmark-inventory | Catalog all known solutions/methods in a domain with performance, applicability, and limitations. |
| enumeration-synthesis | Synthesize all systematic enumeration outputs into a structured idea report with prioritized recommendations. |
| intersection-evaluation | Evaluate exploration status of each cell in a method×problem matrix, annotating as explored, partial, or unexplored. |
| method-problem-crossing | Build method×problem cross-reference matrix showing which methods have been applied to which problems. |
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.
- 9d ago First seen · 81 lines · 24 tokens per session scan A a1c183432914
benchmark-sweep 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 24 tokens to every session and 664 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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