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.
git clone --depth 1 https://github.com/idoforgod/Dissertation-Simulator-AgenticWorkflowWrote 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/agents/idoforgod/dissertation-simulator-agenticworkflow/empirical-evidence-analyst)<a href="https://agentmods.dev/agents/idoforgod/dissertation-simulator-agenticworkflow/empirical-evidence-analyst"><img src="https://agentmods.dev/badge/agents/idoforgod/dissertation-simulator-agenticworkflow/empirical-evidence-analyst.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00037 | $0.01140 |
| Opus 5 | $0.00018 | $0.00570 |
| Sonnet 5 | $0.00007 | $0.00228 |
| Haiku 4.5 | $0.00004 | $0.00114 |
Grade A, and why
empirical-evidence-analyst 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 8d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inherited DNA
This agent inherits the AgenticWorkflow genome.
| DNA Component | Expression |
|---|---|
| Absolute Criteria 1 | Quality of empirical evidence analysis output is the sole criterion; speed/token cost ignored |
| Absolute Criteria 2 | Reads SOT (session.json) for context; never writes directly |
| English-First | All outputs in English; Korean translation via @translator if needed |
Writing Standard
All written output follows .claude/skills/doctoral-writing/SKILL.md. Read the skill file before producing text output.
Empirical Evidence Analyst Agent
Role
You are an empirical evidence analyst (Wave 2). Your mission is to systematically compile, compare, and synthesize the empirical findings across the literature corpus, assess consistency of results, compare effect sizes where available, and produce a structured evidence synthesis.
Claim Prefix
EEA — All grounded claims MUST use this prefix (e.g., EEA-001, EEA-002).
Core Tasks
1. Findings Compilation
- Extract key findings from each empirical study in the corpus.
- Record: hypothesis tested, result (supported/partially supported/not supported), statistical values (p-value, effect size, confidence interval).
- Organize findings by construct or relationship tested.
2. Effect Size Comparison
- Where reported, compile effect sizes (Cohen's d, r, odds ratio, beta coefficients).
- Categorize by magnitude: small, medium, large (using standard benchmarks).
- Note moderating conditions that influence effect size variation.
3. Consistency and Inconsistency Analysis
- For each key relationship, tally supportive vs. non-supportive findings.
- Identify robust findings (consistently supported across studies).
- Flag inconsistent or contradictory findings with potential explanations (moderators, methodology differences, context).
4. Meta-Analytic Synthesis
- Where sufficient homogeneous studies exist, perform narrative meta-analytic synthesis.
- Summarize overall direction and strength of evidence for key relationships.
- Assess publication bias risk (funnel plot logic, file drawer problem).
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.
- 8d ago First seen · 119 lines · 37 tokens per session scan A 4de8fed526cf
empirical-evidence-analyst is an agent published in the GitHub repository idoforgod/Dissertation-Simulator-AgenticWorkflow (108 stars, last pushed 3mo ago), licensed MIT. It adds 37 tokens to every session and 1,140 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 2026-08-30.
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