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 sendralt/agentic-awesome-skills --skill auto-researchgit clone --depth 1 https://github.com/sendralt/agentic-awesome-skillsWrote 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/sendralt/agentic-awesome-skills/auto-research)<a href="https://agentmods.dev/skills/sendralt/agentic-awesome-skills/auto-research"><img src="https://agentmods.dev/badge/skills/sendralt/agentic-awesome-skills/auto-research/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/sendralt/agentic-awesome-skills/auto-research"><img src="https://agentmods.dev/badge/skills/sendralt/agentic-awesome-skills/auto-research.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 | $0.00029 | $0.01265 |
| Opus 5 | $0.00015 | $0.00633 |
| Sonnet 5 | $0.00006 | $0.00253 |
| Haiku 4.5 | $0.00003 | $0.00127 |
Grade A, and why
auto-research 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 5d 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 auto-research — 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto-Research Skill
Overview
When implementing tasks, Claude Code can encounter uncertainties — design choices, algorithm details, API usage, or best practices. This skill provides an explicit-consent research path, presents findings, and waits for user approval before writing code.
The skill supports web research and an optional ChatGPT consultation. It never sends conversation context, files, browser state, or credentials to a third party without the user's explicit approval of the exact, redacted text.
When to Use This Skill
- User asks a question where multiple valid approaches exist
- Claude is uncertain about algorithm details or API usage
- Design/architecture choices need comparison
- The user explicitly asks to search the web or consult ChatGPT and approves the proposed query
How It Works
Step 1: Propose the research boundary — State the source to use, the exact query or redacted prompt, whether any local/workspace text would leave the machine, and the likely cost. Wait for the user to approve that exact boundary.
Step 2: Research — After approval, use web search or a browser session the user has
explicitly selected and authorized. Use a pinned, user-configured browser automation
connector; do not install packages automatically, use @latest, or access browser cookies,
other tabs, saved passwords, or sessions.
Step 3: Present — Distill findings into concise options with sources, presented to the user.
Step 4: Await Approval — Do NOT write code until the user says "go ahead" or picks an option.
Step 5: Implement — Once approved, execute with confidence.
Explicit ChatGPT Consultation
Do not treat ?, ??, or another shorthand as consent. First propose a minimal prompt,
for example: 请评估这个已脱敏的方案的正确性、完整性和可改进之处：<text>.
Explicitly identify every piece of text that would be sent. Only after the user confirms
the exact prompt may you open the selected ChatGPT session, submit that prompt, and present
the response. Do not include conversation history by default.
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.
- 5d ago First seen · 127 lines · 29 tokens per session scan A 657d4845d62c
auto-research is a skill published in the GitHub repository sendralt/agentic-awesome-skills (1 stars, last pushed 2d ago), licensed MIT. It adds 29 tokens to every session and 1,265 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to auto-research, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
auto-research
Research uncertain questions with an explicit, user-approved web search or ChatGPT consultation, then present options and wait for implementation approval.
auto-research
Research uncertain questions with an explicit, user-approved web search or ChatGPT consultation, then present options and wait for implementation approval.
auto-research
Research uncertain questions with an explicit, user-approved web search or ChatGPT consultation, then present options and wait for implementation approval.
auto-research
Research uncertain questions with an explicit, user-approved web search or ChatGPT consultation, then present options and wait for implementation approval.
playwright-cli
Automate browser interactions, test web pages and work with Playwright tests.
moli-webfetch
Fetch, inspect, crawl, and capture live, JavaScript-rendered websites with Moli. Use when Codex needs current web content, web research, fact lookup, link following, a bounded crawl, client-rendered or response-gated content, network diagnostics, or a standalone HTML, Markdown, JSON, semantic-tree, viewport or…