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 bias-detectiongit 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/bias-detection)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/bias-detection"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/bias-detection/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/bias-detection"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/bias-detection.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 123 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 | $0.00039 | $0.01433 |
| Opus 5 | $0.00019 | $0.00717 |
| Sonnet 5 | $0.00008 | $0.00287 |
| Haiku 4.5 | $0.00004 | $0.00143 |
Grade A, and why
bias-detection 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bias Detection Strategy
Design a protocol to systematically assess biases that threaten the validity of meta-analytic conclusions.
Purpose
Bias in the evidence body (publication bias, outcome reporting bias, citation bias, time-lag bias, language bias) can invalidate pooled estimates. This strategy designs the complete bias detection and adjustment protocol — funnel plots, statistical tests, sensitivity analyses, and GRADE certainty downgrading.
Budget
| Resource | Floor | Target |
|---|---|---|
| Studies identified | 28 | 40 |
| Effect sizes extracted | 28 | 40 |
| Web searches | 28 | 40 |
| Bias domains assessed | 5 | 8 |
| Quality assessments | 20 | 40 |
Budget gate: cannot exit until 80% of floor met.
State Ledger
<HARD-GATE>
| Metric | Current | Floor | Target | Status |
|--------|---------|-------|--------|--------|
| Studies found | 0 | 28 | 40 | BLOCKED |
| Effect sizes planned | 0 | 28 | 40 | BLOCKED |
| Web searches done | 0 | 28 | 40 | BLOCKED |
| Bias domains assessed | 0 | 5 | 8 | BLOCKED |
| Quality assessed | 0 | 20 | 40 | BLOCKED |
</HARD-GATE>
Available Tactics
| Tactic | When to Use |
|---|---|
| effect-size-extraction | Extract effect sizes with precision (SE, CI) |
| quality-assessment-protocol | Full RoB2 assessment per study |
| evidence-synthesis-planning | Plan bias-adjusted models |
Available SOPs
| SOP | When to Use |
|---|---|
| pico-formulation | Frame the evidence assessment question |
| inclusion-criteria-design | Include grey literature, preprints |
| effect-size-planning | Ensure precision metrics extracted |
| data-extraction-form | Template capturing reporting completeness |
| risk-of-bias-assessment | Per-study RoB (core of this strategy) |
| publication-bias-assessment | Core SOP — funnel plots, statistical tests |
| sensitivity-analysis-design | Trim-and-fill, selection models |
| heterogeneity-source-analysis | Bias as heterogeneity driver |
| meta-analysis-synthesis | Final bias assessment protocol |
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 · 152 lines · 39 tokens per session scan A 10f82d88c2c4
bias-detection 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 39 tokens to every session and 1,433 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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