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 comparative-formulationgit 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/comparative-formulation)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/comparative-formulation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/comparative-formulation/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/comparative-formulation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/comparative-formulation.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 80 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 | $0.00018 | $0.00759 |
| Opus 5 | $0.00009 | $0.00380 |
| Sonnet 5 | $0.00004 | $0.00152 |
| Haiku 4.5 | $0.00002 | $0.00076 |
Grade A, and why
comparative-formulation 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comparative Formulation
Construct comparative research questions — when research requires comparing A vs B, systematically construct a fair, meaningful comparison.
When to Use
- Need to compare two methods/conditions/groups
- The hypothesis involves "X is better than / different from Y"
- Need to ensure the fairness and validity of the comparison
Thinking Framework
Core logic: a good comparative research question requires clarifying four elements — what is compared (objects), along what dimension (metrics), under what conditions (controls), and what counts as "different" (threshold).
Comparison Design Principles
- Fairness: the comparison conditions are fair to both sides (not a strawman)
- Clear dimensions: along which dimension(s) the comparison is made
- Controlled variables: all conditions are the same except the compared objects
- Effect size: not just "whether there is a difference" but "how large a difference is meaningful"
Comparison Types
| Type | Example | Key considerations |
|---|---|---|
| Method comparison | Method A vs Method B | Implementation fairness, dataset selection |
| Condition comparison | With X vs Without X | Controlled variables, confounding factors |
| Group comparison | Group A vs Group B | Matching, selection bias |
| Temporal comparison | Before vs After | History effects, maturation effects |
Budget Gate
| Tier | Comparison design | Fairness argument | Output |
|---|---|---|---|
| S | Comparison objects + clear dimensions | Basic fairness statement | ≥1 comparative RQ |
| M | + controlled variables + effect size | Fairness argument + identification of potential bias | ≥2 comparative RQs |
| L | + multi-dimensional + sensitivity | Full fairness analysis + bias mitigation strategy | ≥3 comparative RQs |
Default Reference Flow
- Determine the comparison objects (what A and B are)
- Determine the comparison dimensions (along what metrics to compare)
- Determine the control conditions (what to keep constant)
- Argue fairness (whether the comparison is fair)
- Structure it with the PICO framework (the C component is core)
- FINER check
- Define success criteria (what counts as a "meaningful difference")
What ships with it
1 file 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.
- 9d ago First seen · 100 lines · 18 tokens per session scan A 64e0c4d70274
comparative-formulation is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (464 stars, last pushed today), licensed Apache-2.0. It adds 18 tokens to every session and 759 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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