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 skills add YousefNabil-SOC/claude-apex --skill autoresearchgit clone --depth 1 https://github.com/YousefNabil-SOC/claude-apexWrote 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/yousefnabil-soc/claude-apex/autoresearch)<a href="https://agentmods.dev/skills/yousefnabil-soc/claude-apex/autoresearch"><img src="https://agentmods.dev/badge/skills/yousefnabil-soc/claude-apex/autoresearch/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/yousefnabil-soc/claude-apex/autoresearch"><img src="https://agentmods.dev/badge/skills/yousefnabil-soc/claude-apex/autoresearch.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 | $0.00032 | $0.00385 |
| Opus 5 | $0.00016 | $0.00192 |
| Sonnet 5 | $0.00006 | $0.00077 |
| Haiku 4.5 | $0.00003 | $0.00038 |
Grade A, and why
autoresearch 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.
What it actually says
Autoresearch — Autonomous Optimization
Inspired by iterative research methodology. Apply to ANY task with a measurable metric.
Core Loop
1. Define GOAL (what you want to achieve)
2. Define METRIC (how you measure success)
3. Define DIRECTION (higher is better / lower is better)
4. Define VERIFY (how to check the metric)
REPEAT:
a. Make ONE modification
b. Measure the metric
c. If improved: KEEP the change
d. If worsened: DISCARD (revert)
e. Log the attempt and result
f. Choose next modification
UNTIL: goal is met OR no more improvements found
Example Applications
Performance optimization
- Goal: Page load under 2 seconds
- Metric: Lighthouse performance score
- Direction: Higher is better
- Verify: Run Lighthouse audit
Bundle size reduction
- Goal: Main bundle under 200KB
- Metric: Bundle size in KB
- Direction: Lower is better
- Verify: Run build and check output
Test coverage
- Goal: 80%+ code coverage
- Metric: Coverage percentage
- Direction: Higher is better
- Verify: Run test suite with coverage
SEO score
- Goal: All pages score 90+
- Metric: SEO audit score
- Direction: Higher is better
- Verify: Run SEO analysis tool
Rules
- ONE change per iteration (isolate variables)
- ALWAYS measure before and after
- ALWAYS revert failed attempts (no accumulating debt)
- LOG every attempt for learning
- STOP when goal is met or 3 consecutive iterations show no improvement
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 · 60 lines · 32 tokens per session scan A 5723a415d5dc
autoresearch is a skill published in the GitHub repository YousefNabil-SOC/claude-apex (2 stars, last pushed 3mo ago), licensed MIT. It adds 32 tokens to every session and 385 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 2026-09-03.
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