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 causal-modelinggit 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/causal-modeling)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/causal-modeling"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/causal-modeling/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/causal-modeling"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/causal-modeling.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 78 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 | $0.00035 | $0.01094 |
| Opus 5 | $0.00017 | $0.00547 |
| Sonnet 5 | $0.00007 | $0.00219 |
| Haiku 4.5 | $0.00003 | $0.00109 |
Grade A, and why
causal-modeling 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 6d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Causal Modeling
Build causal models for research domains. Identifies variables, maps causal mechanisms, collects supporting evidence, analyzes potential interventions, and validates the resulting causal graph.
Manifest
| Level | Count | Skills |
|---|---|---|
| Strategy | 5 | variable-identification, mechanism-mapping, evidence-collection, intervention-analysis, model-validation |
| Tactic | 3 | counterfactual-reasoning, evidence-weighing, feedback-loop-detection |
| SOP | 10 | variable-page-creation, mechanism-edge-creation, evidence-linking, contradiction-flagging, confidence-scoring, intervention-page-creation, loop-documentation, model-gap-detection, causal-chain-query, validation-report |
Budget Table
| Metric | Small | Medium | Large |
|---|---|---|---|
| Variables identified | 8 | 20 | 40 |
| Causal edges created | 15 | 40 | 80 |
| Evidence pages linked | 10 | 30 | 60 |
| Interventions analyzed | 2 | 5 | 10 |
| Feedback loops documented | 1 | 3 | 6 |
Strategy Sequence (Reference, Not Prescription)
- variable-identification — identify key variables in the causal system
- mechanism-mapping — map causal mechanisms between variables
- evidence-collection — gather evidence supporting/refuting causal claims
- intervention-analysis — analyze what happens when variables are manipulated
- model-validation — validate the causal model for consistency and completeness
MCP Tools Used
vault_search— find existing variables and mechanismsvault_add_edge— create causal edges (derived_from, supported_by, contradicts)vault_query_graph— trace causal chainsvault_graph_stats— assess model coveragevault_lint— validate structural integrity
Context-Management
Guiding Principles
- Correlation is not causation. Every causal edge must have mechanistic justification, not just statistical association.
- Confounders are everywhere. Actively search for confounding variables that could explain observed relationships.
- Interventions reveal truth. The strongest evidence for causation comes from intervention studies.
- Feedback loops are the norm. Most real systems have circular causation. Document loops explicitly.
- Confidence is calibrated. Strong mechanism + strong evidence = high confidence. Weak either = low confidence.
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.
- 6d ago First seen · 113 lines · 35 tokens per session scan A 686aa8c19bc1
causal-modeling is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (449 stars, last pushed today), licensed Apache-2.0. It adds 35 tokens to every session and 1,094 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.
Other skills, from other repositories
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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.…
lit-review-assistant
Search, summarize, and synthesize economics literature.
papers-reading-skill
Evidence-grounded AI research workflow for turning supplied economics, finance, management, and social-science papers or structured records into versioned PaperReading artifacts. Use when Codex must ingest text, Markdown, or a text-based PDF; separate source-grounded claims from researcher analysis; bind findings to…
academic-figure-generation
Generates publication-quality academic figures (framework diagrams, pipeline illustrations, system architectures, method overviews) from a paper's method text and a target caption, using a local PaperBanana multi-agent pipeline (Retriever → Planner → Stylist → Visualizer → Critic).
academic-paper-reviewer
Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on…
research-log
Record, manage, and query research experiment logs. Use when the user wants to log an experiment result, amend an existing entry, view recent logs, rebuild the index, or plan, execute, repeat, or diagnose research in a project containing docs/researchlog/. Triggers on phrases like "log this experiment", "record…