agent-research-aggregator

agent-research-aggregator is a skill for Claude Code, Codex from Bilal140202/the-lord-of-the-skills. It costs 177 tokens per session (3,321 once invoked), scanned A, a copy of agent-research-aggregator, MIT.

A preprocessing tool for PaperOrchestra, a research workflow, that gathers experiment logs from AI coding-agent cache folders. It turns scattered notes and results into structured idea and experiment-log files.

In plain words
What is it for?
Use it to collect agent experiments from folders such as .claude or .cursor and prepare inputs for PaperOrchestra.
Why use it?
It removes the manual work of finding relevant logs, extracting findings and numbers, and formatting them for the next research step.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Not installable: its command points at a path on the author’s own machine, so it runs nowhere else. The line is /home/alice/projects/my-rl-experiment.

Good fit Use it to collect agent experiments from folders such as .claude or .cursor and prepare inputs for PaperOrchestra.

Compare 6 skills from other repositories ↓
Install

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.

Made for: Claude Code, Codex.

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.

agentmods badge for agent-research-aggregator

README.md
[![agentmods](https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/ar9av__paperorchestra/github.svg)](https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/ar9av__paperorchestra)
Your own site
<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/ar9av__paperorchestra"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/ar9av__paperorchestra/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.

agentmods 80×15 button for agent-research-aggregator

Your own site · 80×15
<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/ar9av__paperorchestra"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/ar9av__paperorchestra.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 177 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,321 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 8d ago against content hash c543b867f847, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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 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.

Origin

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.

skills/gondor/claude-code/Ar9av__PaperOrchestra/SKILL.md · 362 lines

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

Read the full file on GitHub · 362 lines

Changes

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.

  1. 8d ago First seen · 362 lines · 177 tokens per session scan A c543b867f847

Subscribe to this mod's changes

agent-research-aggregator is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (4 stars, last pushed 6d 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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