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 agentmods add skills/datalab-atom/evoany/research-loopnpx skills add DataLab-atom/EvoAny --skill research-loopgit clone --depth 1 https://github.com/DataLab-atom/EvoAnyWhat 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 | $0.00038 | $0.01498 |
| Opus 5 | $0.00019 | $0.00749 |
| Sonnet 5 | $0.00008 | $0.00300 |
| Haiku 4.5 | $0.00004 | $0.00150 |
Grade A, and why
research-loop 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 3d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/research-loop — Research Derivation Loop
C4: Core research verification loop — drives the derivation forest from evolution results to deep motivation discovery.
Purpose
Starting from completed evolution results (code changes + performance data), iteratively build a derivation forest to discover why the changes work, find deep motivations, and grade contributions for paper writing.
Usage
/research-loop [evo_session_id]
If no session ID given, uses the current/most recent evolution session.
Behavior
Phase 1: Initialization
- Call
evo_get_statusto get evolution results summary - Call
evo_get_lineagefor the best branch to understand the full change history - Call
research_init_forestto create a new derivation forest - For each significant code change in the lineage:
- Call
research_add_node(type="change")to register it as a root node
- Call
Phase 2: Iterative Exploration (main loop)
Each iteration performs 5 steps:
Step 1 — Cut into code changes
- Read the current active change nodes
- Use
code_qato understand what each change does - May merge or split change nodes via
research_merge_nodes - Add refined
changenodes as needed
Step 2 — Reverse reasoning: why does it work?
- For each active change node, hypothesize why it improves performance
- Call
research_add_node(type="hypothesis", parent_ids=[change_id]) - Consider: what unsolved domain problem does this address?
Step 3 — Literature search
- For each new hypothesis, call
/ask-litwith the hypothesis as query - Call
research_add_node(type="evidence", literature_refs=[...])for each finding - Update hypothesis nodes with literature context
Step 4 — Experimental verification & visualization
- Design targeted experiments (ablation / control) to test hypotheses
- Call
bench_adapt+bench_runto execute experiments - Call
bench_validateto check result reasonableness - Call
viz_generateto generate ablation curves, score distributions, and contribution heatmaps for each confirmed hypothesis. Save outputs toresearch/figures/. - Call
viz_polishto polish the figures for publication quality. - Record figure paths in evidence nodes so write-experiment can reference them later.
- Supported:
research_update_node(status="pruned")for rejected hypotheses - Supported: continue deepening for confirmed hypotheses
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.
- 3d ago First seen · 157 lines · 38 tokens per session scan A bf2c05da1361
research-loop is a skill published in the GitHub repository DataLab-atom/EvoAny (37 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 1,498 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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