Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/woodfishhhh/ez_math_model/agent-research-aggregator)<a href="https://agentmods.dev/skills/woodfishhhh/ez_math_model/agent-research-aggregator"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/agent-research-aggregator/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/woodfishhhh/ez_math_model/agent-research-aggregator"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/agent-research-aggregator.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 | $0.00177 | $0.03321 |
| Opus 5 | $0.00088 | $0.01661 |
| Sonnet 5 | $0.00035 | $0.00664 |
| Haiku 4.5 | $0.00018 | $0.00332 |
Grade A, and why
agent-research-aggregator 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.
This is a copy
100% identical to agent-research-aggregator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agent-research-aggregator
Should I run? (decision gate)
Before starting Phase 1, check whether aggregation is actually needed:
| Situation | Action |
|---|---|
workspace/inputs/idea.md and workspace/inputs/experimental_log.md both exist and are non-empty |
Skip this skill entirely. Proceed directly to paper-orchestra. |
| Either file is missing or empty, and the user provided a directory path | Run this skill with that directory as --search-roots. |
| Either file is missing or empty, and no directory was provided | Scan cwd and ~ by default; show the discovery summary to the user before continuing. |
| The inputs exist but look thin (e.g. idea.md has < 5 lines, no numeric data in experimental_log.md) | Ask the user whether to supplement with aggregation or proceed as-is. |
The skill is intentionally a pre-pass — it is cheap to skip and should only run when the structured inputs don't already exist.
A pre-processing skill for PaperOrchestra (arXiv:2604.05018). Reads scattered
experimentation artifacts from AI coding-agent cache directories and synthesizes
them into the structured (I, E) input pair the PaperOrchestra pipeline expects.
[.claude/] [.cursor/] [.antigravity/] [.openclaw/]
│ │ │ │
└────────────┴──────────────┴───────────────┘
│
Phase 1: Discovery
(discover_logs.py)
│
discovered_logs.json
│
Phase 2: Extraction
(LLM call per log batch)
│
raw_experiments.json
│
Phase 3: Synthesis
(LLM call — consolidate)
│
synthesis.json
│
Phase 4: Formatting
(format_po_inputs.py)
│
┌────────────┴────────────┐
workspace/inputs/ workspace/ara/
idea.md aggregation_report.md
experimental_log.md discovered_logs.json
raw_experiments.json
synthesis.json
What ships with it
6 files 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.
- 10d ago First seen · 362 lines · 177 tokens per session scan A c543b867f847
agent-research-aggregator is a skill published in the GitHub repository woodfishhhh/EZ_math_model (41 stars, last pushed 1mo ago), licensed MIT. It adds 177 tokens to every session and 3,321 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to agent-research-aggregator, differing in 0 lines, and is treated as a copy.
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