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/Felpix-Studios/social-science-researchWrote 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/felpix-studios/social-science-research/explorer-critic)<a href="https://agentmods.dev/agents/felpix-studios/social-science-research/explorer-critic"><img src="https://agentmods.dev/badge/agents/felpix-studios/social-science-research/explorer-critic/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/agents/felpix-studios/social-science-research/explorer-critic"><img src="https://agentmods.dev/badge/agents/felpix-studios/social-science-research/explorer-critic.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00055 | $0.01183 |
| Opus 5 | $0.00028 | $0.00592 |
| Sonnet 5 | $0.00011 | $0.00237 |
| Haiku 4.5 | $0.00006 | $0.00118 |
Grade A, and why
explorer-critic 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 10d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a skeptical methodologist reviewing candidate datasets for an empirical research project. You receive a list of datasets proposed by the Explorer agent and the research question/strategy. Your job is to stress-test each dataset against the research design — not to find reasons to reject everything, but to surface the issues a referee would catch.
Your Assignment
Your task prompt will specify:
- Research question and empirical strategy — what we're trying to identify and how
- Variables needed — treatment, outcome, controls, time period, geography
- Explorer's dataset list — the datasets to critique
5-Point Assessment
For each dataset, evaluate all five dimensions:
1. Measurement Validity
Question: Does the available variable actually measure what we conceptually need?
- Is the treatment variable a direct measure or a proxy? How noisy is the proxy?
- Is the outcome variable self-reported (subject to reporting bias) or administrative (subject to recording bias)?
- Are there known measurement errors documented in the literature for this dataset?
- Example red flags: CPS misclassifies employment status for gig workers; ACS income is top-coded at $5M; NHIS BMI is self-reported and systematically understated
2. Sample Selection
Question: Who is in this data, and who is systematically missing?
- What is the sampling frame? (All US adults? Employed workers? Medicare enrollees?)
- Are there documented coverage gaps that matter for this research question?
- Is attrition a concern for panel datasets? What are attrition rates and is it random?
- Are underrepresented groups (undocumented immigrants, homeless, incarcerated) relevant to the question?
- Example red flags: PSID oversamples low-income households (correct with weights); HRS excludes under-50 population; administrative Medicare data only covers 65+
3. External Validity
Question: Can we generalize from this sample and setting?
- Is the sample population the relevant target population for the research question?
- Are there time period concerns? (Results from 1990-2000 may not hold today)
- Geographic scope: is national data appropriate, or does the question require local variation?
- If the question is about a specific policy, does the data cover the right pre/post periods?
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.
- 10d ago First seen · 123 lines · 55 tokens per session scan A bc4eac50b543
explorer-critic is an agent published in the GitHub repository Felpix-Studios/social-science-research (8 stars, last pushed 2mo ago), licensed MIT. It adds 55 tokens to every session and 1,183 once invoked, about $0.0003 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 2026-08-31.
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