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 baseline-establishmentgit 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/baseline-establishment)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/baseline-establishment"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/baseline-establishment/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/baseline-establishment"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/baseline-establishment.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 97 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 | $0.00042 | $0.01104 |
| Opus 5 | $0.00021 | $0.00552 |
| Sonnet 5 | $0.00008 | $0.00221 |
| Haiku 4.5 | $0.00004 | $0.00110 |
Grade A, and why
baseline-establishment 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Baseline Establishment
Strategy Routing
| User Intent | Route To |
|---|---|
| Find all methods for a task | method-inventory |
| Extract scores from papers | performance-extraction |
| Normalize conditions across papers | condition-standardization |
| Check reproducibility / discrepancies | discrepancy-analysis |
| Track progress over time / headroom | progress-quantification |
Manifest
Strategies (5)
| Strategy | Purpose |
|---|---|
| method-inventory | Comprehensively identify all relevant methods for a task |
| performance-extraction | Systematically extract performance data and conditions from papers |
| condition-standardization | Standardize evaluation condition differences across papers |
| discrepancy-analysis | Identify discrepancies between reported and reproducible scores |
| progress-quantification | Track performance progress over time, quantify remaining headroom |
Tactics (3)
| Tactic | Purpose |
|---|---|
| leaderboard-harvesting | Systematically collect performance data from platforms and papers |
| condition-normalization | Compare and standardize experimental conditions across papers |
| progress-curve-construction | Build performance-over-time progress curves |
Subagent SOPs (10)
| SOP | Purpose |
|---|---|
| method-discovery | Identify methods via literature, leaderboards, citation chains |
| score-extraction | Extract (Task, Dataset, Metric, Score, Conditions) tuples |
| condition-cataloging | Record evaluation conditions per method |
| reproducibility-checklist-audit | Assess paper against ML Reproducibility Checklist |
| performance-table-assembly | Assemble unified comparison table |
| compute-normalization | Normalize results by compute budget |
| discrepancy-identification | Compare same-method scores across sources |
| headroom-estimation | Estimate ceiling vs current SOTA gap |
| progress-curve-fitting | Construct performance-over-time data |
| baseline-synthesis | Produce final structured baseline report |
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 · 121 lines · 42 tokens per session scan A 771d8a2317d1
baseline-establishment 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 42 tokens to every session and 1,104 once invoked, about $0.0002 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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