BrowserAct is a browser automation system that lets AI agents use real, separate browser sessions to extract data and complete account-based web tasks. It is for agents and teams that need parallel workflows, reused login states, human handoffs, or access to sites that resist ordinary automated requests.
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 browser-act/skills --skill x-dm-auto-chatgit clone --depth 1 https://github.com/browser-act/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/browser-act/skills/x-dm-auto-chat)<a href="https://agentmods.dev/skills/browser-act/skills/x-dm-auto-chat"><img src="https://agentmods.dev/badge/skills/browser-act/skills/x-dm-auto-chat/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/browser-act/skills/x-dm-auto-chat"><img src="https://agentmods.dev/badge/skills/browser-act/skills/x-dm-auto-chat.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00128 | $0.03710 |
| Opus 5 | $0.00064 | $0.01855 |
| Sonnet 5 | $0.00026 | $0.00742 |
| Haiku 4.5 | $0.00013 | $0.00371 |
Grade A, and why
x-dm-auto-chat 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.
How it starts
The opening of the file, as written. The whole thing — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.
X (Twitter) — DM Auto Chat (End-to-End)
Full X DM automation Skill: inbox scan → conversation read → persona-based reply → send; also supports search-and-outreach. The calling Agent generates reply text based on persona; this Skill handles all mechanical operations.
Language
All process output to user (progress updates, process notifications) follows the user's language.
Objective
Encapsulate "refresh DM list → identify pending replies → read context → reply with persona → send" and "search user → enter chat → send first message" into callable end-to-end capabilities.
Prerequisites
- Browser is open at X site, logged into X account (
[aria-label="Account menu"]present) - The 4-digit DM passcode for the current account is available (required for E2E encryption)
- Caller has prepared a "persona description" (used to generate replies), e.g.:
"You are BrowserAct outreach team. Tone: friendly, concise, professional. Goal: invite creators to collaborate."
- Optional: list of target user search queries (for outreach scenario)
Pre-execution Checks
1. Tool Readiness
If browser-act has been confirmed available in the current session → skip.
Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.
2. Open DM Entry + Comprehensive State Check
browser-act --session <name> navigate https://x.com/i/chat
browser-act --session <name> wait stable --timeout 15000
browser-act --session <name> eval "$(python scripts/check-page-state.py)"
Return format:
{
"url": "https://x.com/i/chat/pin/recovery?from=%2Fi%2Fchat",
"logged_in": true,
"need_passcode": true,
"on_inbox": false,
"on_conversation": false,
"has_panel": false,
"has_composer": false,
"inbox_count": 0
}
Decision matrix:
logged_in: false→ inform user to log in first; wait; retry this stepneed_passcode: true→ proceed to step 3 belowon_inbox: trueandinbox_count > 0→ ready, enter business flowon_inbox: truebutinbox_count === 0→ account has no DM conversations; outreach scenario can still proceed, pending-reply scenario has nothing to do
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- scripts/check-composer.py 2.2 KB runs code
- scripts/check-page-state.py 1.2 KB runs code
- scripts/fetch-inbox-api.py 4.7 KB runs code
- scripts/open-conversation-by-user.py 1.3 KB runs code
- scripts/read-conversation.py 3.5 KB runs code
- scripts/scan-inbox-merged.py 5.5 KB runs code
- scripts/scroll-load-history.py 1.5 KB runs code
- scripts/search-users.py 2.8 KB runs code
- scripts/verify-sent.py 3.0 KB runs code
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 · 271 lines · 128 tokens per session scan A 5a8b8efe012c
x-dm-auto-chat is a skill published in the GitHub repository browser-act/skills (5,832 stars, last pushed 15d ago), licensed MIT. It adds 128 tokens to every session and 3,710 once invoked, about $0.0006 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.
Other skills, from other repositories
skill-monitor
Analyze skill effectiveness across sessions. Computes per-skill metrics (action rate, friction, outcomes), identifies degrading skills, and generates improvement recommendations. Requires session-scan data in metrics.jsonl.
audit
Project health audit and health check — architecture, performance, tests, dependencies, code quality. Use when assessing overall project health, before releases, or after refactors.
learn-from-fix
Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning, record a lesson, prevent a fixed mistake, or remember package guidance with --library.
phx-deps-audit
Audit Hex deps for supply-chain security risk — bidi chars, compile-time exec, maintainer changes, typosquats, CVEs. Use after mix deps.update, when checking if a package upgrade is safe, or reviewing mix.lock PR diffs.
promote
Generate X/Twitter release promotion posts with ASCII tables and CodeSnap rendering. Use when writing release posts, promotion tweets, plugin announcements, or preparing social media content for new versions.
release
CONTRIBUTOR TOOL - Cut a plugin release: bump plugin.json version, finalize CHANGELOG, update README if needed, gate on make ci, commit, tag vX.Y.Z, and create the GitHub release. Use when shipping a new plugin version. NOT distributed.