Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/raja21068/AutoResearchnpx agentmods add skills/raja21068/autoresearch/meta-optimizeWrote 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/raja21068/autoresearch/meta-optimize)<a href="https://agentmods.dev/skills/raja21068/autoresearch/meta-optimize"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/meta-optimize/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/raja21068/autoresearch/meta-optimize"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/meta-optimize.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 | $0.00081 | $0.02724 |
| Opus 5 | $0.00041 | $0.01362 |
| Sonnet 5 | $0.00016 | $0.00545 |
| Haiku 4.5 | $0.00008 | $0.00272 |
Grade A, and why
meta-optimize 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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meta-Optimize: Outer-Loop Harness Optimization for ARIS
Analyze accumulated usage logs and propose optimizations for: $ARGUMENTS
Context
ARIS is a research harness — a system of skills, bridges, workflows, and artifact contracts that wraps around LLMs to orchestrate research. This skill implements a prototype outer loop that observes how the harness is used and proposes improvements to the harness itself (not to the research artifacts it produces).
Inspired by Meta-Harness (Lee et al., 2026): the key insight is that harness design matters as much as model weights, and harness engineering can be partially automated by logging execution traces and using them to guide improvements.
What This Skill Optimizes (Harness Components)
| Component | Example | Optimizable? |
|---|---|---|
| SKILL.md prompts | Reviewer instructions, quality gates, step descriptions | Yes |
| Default parameters | difficulty: medium, MAX_ROUNDS: 4, threshold: 6/10 |
Yes |
| Convergence rules | When to stop the review loop, retry counts | Yes |
| Workflow ordering | Skill chain sequence within a workflow | Yes |
| Artifact schemas | What fields go in EXPERIMENT_LOG.md, idea-stage/IDEA_REPORT.md | Cautious |
| MCP bridge config | Which reviewer model, routing rules | No (infra) |
Not optimized: The research artifacts themselves (papers, code, experiments). That's what the regular workflows do.
Prerequisites
- Logging must be active. Copy
templates/claude-hooks/meta_logging.jsoninto your project's.claude/settings.json(or merge the hooks section). - Sufficient data. At least 5 complete workflow runs logged in
.aris/meta/events.jsonl. The skill will check and warn if insufficient.
Workflow
Step 0: Check Data Availability
EVENTS_FILE=".aris/meta/events.jsonl"
if [ ! -f "$EVENTS_FILE" ]; then
echo "ERROR: No event log found at $EVENTS_FILE"
echo "Enable logging first: copy templates/claude-hooks/meta_logging.json into .claude/settings.json"
exit 1
fi
EVENT_COUNT=$(wc -l < "$EVENTS_FILE")
SKILL_INVOCATIONS=$(grep -c '"skill_invoke"' "$EVENTS_FILE" || echo 0)
SESSIONS=$(grep -c '"session_start"' "$EVENTS_FILE" || echo 0)
echo "📊 Event log: $EVENT_COUNT events, $SKILL_INVOCATIONS skill invocations, $SESSIONS sessions"
if [ "$SKILL_INVOCATIONS" -lt 5 ]; then
echo "⚠️ Insufficient data (<5 skill invocations). Continue using ARIS normally and re-run later."
exit 0
fi
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 · 254 lines · 81 tokens per session scan A f20aa67a3554
meta-optimize is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 81 tokens to every session and 2,724 once invoked, about $0.0004 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 2026-09-03.
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paper-orchestra
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section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…