Eclipse Debug
gradusnikov/eclipse-chatgpt-plugin
Debug Java applications in Eclipse — set breakpoints, launch in debug mode, step through code, inspect stack traces, evaluate expressions, and hot-swap code changes.
Agent Loop for cross-device sync/transfer issues: collect environment fingerprints from BOTH machines first, check memory for known patterns, manage hypotheses with forced falsification, and persist…
The automated check flagged lines worth reading first. See the safety section below.
$ npx skills add UniClipboard/UniClipboard --skill cross-device-diagnose -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install UniClipboard/UniClipboard cross-device-diagnose --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/UniClipboard/UniClipboard.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/cross-device-diagnose .claude/skills/cross-device-diagnose && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "cross-device-diagnose" agent skill from https://github.com/UniClipboard/UniClipboard/tree/main/.agents/skills/cross-device-diagnose into .claude/skills/cross-device-diagnose/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cross-device-diagnose", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/UniClipboard/UniClipboard/tree/main/.agents/skills/cross-device-diagnoseType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add UniClipboard/UniClipboard --skill cross-device-diagnose -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install UniClipboard/UniClipboard cross-device-diagnose --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/UniClipboard/UniClipboard.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/cross-device-diagnose .agents/skills/cross-device-diagnose && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cross-device-diagnose" agent skill from https://github.com/UniClipboard/UniClipboard/tree/main/.agents/skills/cross-device-diagnose into .agents/skills/cross-device-diagnose/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cross-device-diagnose", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add UniClipboard/UniClipboard --skill cross-device-diagnose -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install UniClipboard/UniClipboard cross-device-diagnose --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/UniClipboard/UniClipboard.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/cross-device-diagnose .cursor/skills/cross-device-diagnose && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "cross-device-diagnose" agent skill from https://github.com/UniClipboard/UniClipboard/tree/main/.agents/skills/cross-device-diagnose into .cursor/skills/cross-device-diagnose/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cross-device-diagnose", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/UniClipboard/UniClipboard.git --path .agents/skills/cross-device-diagnose--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add UniClipboard/UniClipboard --skill cross-device-diagnose -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install UniClipboard/UniClipboard cross-device-diagnose --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/UniClipboard/UniClipboard.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/cross-device-diagnose .gemini/skills/cross-device-diagnose && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "cross-device-diagnose" agent skill from https://github.com/UniClipboard/UniClipboard/tree/main/.agents/skills/cross-device-diagnose into .gemini/skills/cross-device-diagnose/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cross-device-diagnose", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install UniClipboard/UniClipboard cross-device-diagnoseInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add UniClipboard/UniClipboard --skill cross-device-diagnose -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/UniClipboard/UniClipboard.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/cross-device-diagnose .github/skills/cross-device-diagnose && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "cross-device-diagnose" agent skill from https://github.com/UniClipboard/UniClipboard/tree/main/.agents/skills/cross-device-diagnose into .github/skills/cross-device-diagnose/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cross-device-diagnose", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add UniClipboard/UniClipboard --skill cross-device-diagnose -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install UniClipboard/UniClipboard cross-device-diagnose --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/UniClipboard/UniClipboard.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/cross-device-diagnose .opencode/skills/cross-device-diagnose && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "cross-device-diagnose" agent skill from https://github.com/UniClipboard/UniClipboard/tree/main/.agents/skills/cross-device-diagnose into .opencode/skills/cross-device-diagnose/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cross-device-diagnose", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
cross-device-diagnoseAgent Loop for cross-device sync/transfer issues: collect environment fingerprints from BOTH machines first, check memory for known patterns, manage hypotheses with forced falsification, and persist…
Cross Device Diagnose is an agent skill from UniClipboard/UniClipboard. Agent Loop for cross-device sync/transfer issues: collect environment fingerprints from BOTH machines first, check memory for known patterns, manage hypotheses with forced falsification, and persist evidence via $wrap. Orchestrates dual-side-debug + local-log-debug + systematic-debugging into a structured loop.
Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Frontend & Design, covering Responsive design, Debugging and Autonomous loops. The repository describes itself as: Real-time clipboard sync across all your devices — local-first, peer-to-peer, and end-to-end encrypted. No account. No cloud dependency. No central server. The licence is AGPL-3.0.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit add157e. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
sshFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use ssh, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Cross Device Diagnose loads about 3.8k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 1,116 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found patterns that need a careful read before installing.
Use `ssh win` (relies on `~/.ssh/config`). If password is needed:Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from UniClipboard/UniClipboard at commit add157e, republished under its AGPL-3.0 licence (© UniClipboard). 1,116 words, ~3,810 tokens.
.claude/skills/cross-device-diagnose/SKILL.md (or your agent's skills folder).An Agent Loop that orchestrates cross-device debugging with a disciplined, evidence-based process. It eliminates the recurring pattern where 50% of hypotheses are wrong, environment issues masquerade as code bugs, and debug context is lost across sessions.
Observed anti-patterns (from 50 recent sessions):
The loop:
1. Environment fingerprint (BOTH machines) ← catches 40% of issues upfront
2. Memory pattern matching ← avoids re-investigating known issues
3. Hypothesis registration + prior ranking ← parallel hypotheses, not serial guessing
4. Evidence collection (logs, state) ← using existing tools
5. Forced falsification ← try to DISPROVE before concluding
6. Loop or escalate/cross-device-diagnose — start the diagnostic looplocal-log-debug + systematic-debuggingerror-diagnose-fixpr-greenlightdual-side-debug directly/tmp/codex-xdd-state.json:
{
"started_at": "ISO timestamp",
"round": 0,
"max_rounds": 5,
"symptom": "Windows restore does not sync to Mac within 3s",
"environment": {
"mac": { "fingerprint_collected": true, "data": {} },
"win": { "fingerprint_collected": true, "data": {} }
},
"memory_matches": [],
"hypotheses": [
{
"id": 1,
"description": "iroh relay path instead of direct LAN connection",
"prior": "high",
"status": "active|confirmed|ruled_out",
"evidence_for": [],
"evidence_against": [],
"falsification_test": "check conn_type in logs for direct IP vs relay"
}
],
"evidence_ledger": [
{
"round": 0,
"source": "mac-env-fingerprint",
"observation": "Clash TUN active, 198.18.0.1 is default gateway",
"hypothesis_impact": { "1": "supports" }
}
],
"ssh_config": {
"host": "win",
"needs_password": true
}
}Before looking at ANY logs or code, collect environment fingerprints from both machines. This catches proxy/VPN/network issues that masquerade as application bugs.
Run all of these in parallel:
# Network interfaces and IPs
ifconfig | grep -E 'flags|inet ' | grep -B1 'inet '
# Default route
netstat -rn | grep default | head -3
# DNS configuration
scutil --dns | grep 'nameserver' | head -5
# Proxy/VPN detection
pgrep -lf 'clash|mihomo|v2ray|tailscale|wireguard|openvpn' 2>/dev/null
networksetup -getwebproxy Wi-Fi 2>/dev/null
networksetup -getsocksfirewallproxy Wi-Fi 2>/dev/null
# Check for TUN interfaces (198.18.x = Clash fake-ip, 100.x = Tailscale)
ifconfig | grep -A2 'utun\|tun' | grep inet
# Uniclipboard daemon status
pgrep -lf uniclip 2>/dev/null
.agents/skills/local-log-debug/uc-logs.sh status 2>/dev/nullssh win "ipconfig & netstat -rn | findstr 0.0.0.0 & tasklist | findstr /i \"clash mihomo tailscale wireguard v2ray\" & netstat -an | findstr 42720"If SSH fails or needs password, ask the user ONCE and note the config in state. Do not ask again.
Build a structured assessment:
Environment Assessment:
Mac:
LAN IP: 192.168.1.100 (en0, Wi-Fi)
Proxy: Clash TUN active (utun3, 198.18.0.1 gateway) ⚠️
Tailscale: active (100.114.7.75 on utun4) ⚠️
Daemon: running (PID 12345, profile=dev, log fresh)
Windows:
LAN IP: 192.168.1.129 (Ethernet)
Proxy: Clash active (PID 5678) ⚠️
Tailscale: not detected ✓
Daemon: running (profile=dev)
⚠️ Both machines have Clash TUN active.
Known issue: TUN mode hijacks UDP (198.18.0.1) and breaks iroh hole-punching.
See memory: lan-sync-slow-tun-proxy-tailscale.mdRead the MEMORY.md index and scan for relevant entries:
grep -i "sync\|slow\|proxy\|tun\|tailscale\|relay\|transfer\|clipboard\|restore" \
${CODEX_HOME:-$HOME/.codex}/memories/MEMORY.mdFor each match, read the memory file and check if the current symptom fits. Known patterns in this project:
| Memory | Pattern | Quick check |
|---|---|---|
lan-sync-slow-tun-proxy-tailscale.md | LAN slow = both sides have TUN proxy | Check for 198.18.0.1 gateway |
presence-asymmetry-and-restart-red-herrings.md | Text works but images don't = blob channel issue | Test text vs image separately |
mobile-sync-file-becomes-url.md | File copied → URL received = outbound meta/file fork | Check entry type on sender |
issue1029-image-xpm-undecodable.md | Image syncs "successfully" but can't paste = wrong MIME | Check image MIME in logs |
macos-text-dedup-permanent-swallows-recopy.md | Re-copy same text = watcher dedup swallows it | Check for MEANINGFUL_REDEDUP |
iroh-production-perf-gotchas.md | Hairpin NAT, CUBIC/BBR3, FsStore blocking | Check conn_type stability |
If a memory matches, report it immediately — it may short-circuit the entire investigation.
Based on the symptom + environment fingerprint + memory matches, register ALL plausible hypotheses at once (not just one):
Hypotheses (ranked by prior probability):
H1 [HIGH] Clash TUN intercepting iroh UDP
Prior: Both machines have TUN active; known issue in memory
Falsification: Disable Clash on one side, retry
H2 [MEDIUM] iroh selecting relay instead of direct LAN path
Prior: conn_type logs previously showed relay/LAN oscillation
Falsification: grep conn_type in logs, check if direct IP used
H3 [LOW] Application-layer bug in restore dispatch
Prior: Only if H1/H2 ruled out; restore logic was recently refactored
Falsification: Check dispatch logs for error/skip/timeout| Prior | When to assign |
|---|---|
| HIGH | Environment fingerprint shows a known issue, OR memory match is exact |
| MEDIUM | Symptom is consistent but environment looks clean; needs log evidence |
| LOW | Requires a code bug in recently-tested logic; unlikely but possible |
Always test HIGH-prior hypotheses first. This is the key efficiency gain — environment issues are caught before wasting rounds on code investigation.
For environment issues, run quick experiments first:
# H1: Can the machines reach each other directly on LAN?
ping -c 3 192.168.1.129
# H1: Is iroh using direct connection?
.agents/skills/dual-side-debug/dual-logs.sh grep "conn_type" --lines 20
# H2: What address is iroh connecting to?
.agents/skills/dual-side-debug/dual-logs.sh grep "connect selected path" --lines 10Use the existing tools — don't hand-roll:
# Time-aligned view around the symptom
.agents/skills/dual-side-debug/dual-logs.sh merge --since "2026-06-21T10:00:00Z" --lines 400
# Filter to relevant subsystem
.agents/skills/dual-side-debug/dual-logs.sh query --filter '.target | test("sync|dispatch|transfer|restore")'
# Errors only
.agents/skills/dual-side-debug/dual-logs.sh query --filter '.level == "ERROR" or .level == "WARN"'Every piece of evidence goes into the ledger with its hypothesis impact:
{
"round": 1,
"source": "dual-logs merge",
"observation": "conn_type = Ip(100.79.191.42:56445) — Tailscale address, not LAN",
"hypothesis_impact": {
"H1": "strongly supports",
"H2": "supports (relay not used, but wrong IP chosen)",
"H3": "neutral"
}
}Before declaring a root cause, actively try to disprove it.
For each hypothesis marked as "supported by evidence":
State the falsification test: "If H1 is correct, then disabling Clash should immediately improve speed. If speed doesn't improve, H1 is wrong."
Run the test (or ask the user to run it if it requires their action):
To test H1, please:
1. Disable Clash on your Mac (quit the app or toggle TUN off)
2. Restart the uniclipboard daemon
3. Try the sync again
If it's still slow after this, H1 is ruled out.Record the result:
ruled_out, never revisitWhen a hypothesis has:
→ Declare root cause with confidence level:
Root cause identified (HIGH confidence):
H1: Clash TUN intercepting iroh UDP traffic
Evidence:
✓ Both machines have TUN active (env fingerprint)
✓ iroh conn_type using 100.x Tailscale address instead of 192.168.x LAN
✓ Known pattern from memory (lan-sync-slow-tun-proxy-tailscale.md)
✓ Falsification survived: disabling Clash on Mac → sync improved to 17MB/s
Recommended fix:
- Short term: disable Clash TUN when using uniclipboard
- Long term: filter Clash fake-ip (198.18.0.0/15) from iroh candidatesIf no hypothesis is conclusive after a round:
If stuck after 3 rounds (same hypotheses, no new evidence):
⚠️ Diagnosis inconclusive after 3 rounds.
Active hypotheses:
H2 [MEDIUM] iroh relay path — some evidence but not conclusive
H3 [LOW] application bug — no evidence for or against
Ruled out:
H1 ✗ Clash TUN — falsified (still slow after disabling)
Suggested next steps:
A) Add diagnostic tracing to the suspect code path and reproduce
B) Run a minimal reproduction (p2p-bench between the two machines)
C) Escalate to iroh upstream (if the issue is in the networking layer)rm -f /tmp/codex-xdd-state.jsonReport all findings, ruled-out hypotheses, and remaining unknowns. Suggest whether to file an issue or continue in a focused session.
When the session ends (user says "enough for now" or switches tasks), remind them to $wrap. The debug state from this skill's state file should be captured in $wrap's active-task.json under the debug section:
"debug": {
"active": true,
"symptom": "Windows restore → Mac sync delay ~3.4s",
"hypotheses_tried": ["H1: Clash TUN (ruled out)", "H2: relay path (partially confirmed)"],
"hypotheses_ruled_out": ["H1"],
"evidence": ["conn_type=Ip(100.x)", "ping LAN=1ms", "Clash disabled no improvement"],
"current_hypothesis": "H2: iroh candidate selection prefers Tailscale over LAN"
}The next session's $continue-task will present this debug state, and this skill can resume from round N instead of restarting.
Ask the user for SSH details exactly once:
To diagnose both sides, I need SSH access to the Windows machine.
Host: win (or IP?)
Password needed? (will not be stored in state file)Use ssh win (relies on ~/.ssh/config). If password is needed:
sshpass -p "$WIN_PASS" ssh win "<command>"Never store the password in the state file or any persisted document.
cmd.exe, not PowerShellfindstr instead of greptype instead of cattasklist instead of ps%LOCALAPPDATA%\app.uniclipboard.desktop-dev\logs\powershell -Command "..." explicitly| Skill | Role in this loop |
|---|---|
dual-side-debug | Tool: fetches and merges logs from both machines |
local-log-debug | Tool: reads single-machine logs |
systematic-debugging | Methodology: Phase 4 falsification discipline comes from here |
$wrap | Persistence: saves debug state for cross-session continuity |
$continue-task | Resume: restores debug state to avoid re-investigating |
error-diagnose-fix | Not used: that's for build errors, not runtime/sync issues |
© UniClipboard, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .agents/skills/cross-device-diagnose of UniClipboard/UniClipboard.
Open the folder on GitHubat commit add157e
Cross Device Diagnose next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Cross Device Diagnose this skillUniClipboard/UniClipboard | 1.9k | — | ~3.8k | Automated safety check: Warn | AGPL-3.0 | |
| Eclipse Debuggradusnikov/eclipse-chatgpt-plugin | 172 | — | ~1.3k | Automated safety check: Pass | MIT | |
| Tui Bug Huntuw-syfi/vibesys | 105 | — | ~6.5k | Automated safety check: Pass | MIT | |
| Debug Microflowsmendixlabs/mxcli | 129 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Tabz BrowserGGPrompts/TabzChrome | 147 | — | ~730 | Automated safety check: Pass | MIT | |
| Debugging Codesickn33/agentic-awesome-skills | 47k | 1 repos | ~3.1k | Automated safety check: Pass | MIT |
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Push the current branch and open a GitHub pull request against main.
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Inspect uniclipboard logs from BOTH the macOS host and the mounted Windows peer when debugging cross-platform sync, pairing, transfer, or daemon issues.
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Drive the UniClipboard iOS app in a simulator and read its OSLog yourself to diagnose a mobile-sync bug, instead of asking the user to paste logs.
Categories
Agent Loop for cross-device sync/transfer issues: collect environment fingerprints from BOTH machines first, check memory for known patterns, manage hypotheses with forced falsification, and persist…. Cross Device Diagnose is an agent skill from UniClipboard/UniClipboard. Agent Loop for cross-device sync/transfer issues: collect environment fingerprints from BOTH machines first, check memory for known patterns, manage hypotheses with forced falsification, and persist evidence via $wrap.
Cross Device Diagnose fits situations like: tasks that involve Responsive design; tasks that involve Debugging; tasks that involve Autonomous loops.
Run `npx skills add UniClipboard/UniClipboard --skill cross-device-diagnose -a claude-code`. Or copy the skill folder (.agents/skills/cross-device-diagnose in UniClipboard/UniClipboard) into .claude/skills/cross-device-diagnose in your project. Claude Code loads it when a task matches its description.
Run `npx skills add UniClipboard/UniClipboard --skill cross-device-diagnose -a codex`. Or copy the skill folder (.agents/skills/cross-device-diagnose in UniClipboard/UniClipboard) into .agents/skills/cross-device-diagnose in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add UniClipboard/UniClipboard --skill cross-device-diagnose -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cross-device-diagnose, .gemini/skills/cross-device-diagnose, .github/skills/cross-device-diagnose and .opencode/skills/cross-device-diagnose in your project.
Going by SKILL.md and its folder, Cross Device Diagnose needs the command-line tools its instructions call (ssh).
SKILL.md contains no URLs. Its commands use ssh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md flagged 1 warning(s): mentions a credentials file (ssh keys, cloud or package-manager tokens). Read the flagged lines before installing; the check is not a guarantee either way.
Cross Device Diagnose is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Cross Device Diagnose: Eclipse Debug (gradusnikov/eclipse-chatgpt-plugin, 172 stars), Tui Bug Hunt (uw-syfi/vibesys, 105 stars), Debug Microflows (mendixlabs/mxcli, 129 stars) and Tabz Browser (GGPrompts/TabzChrome, 147 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
UniClipboard (a GitHub organization) maintains it in UniClipboard/UniClipboard, which has 1,867 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on October 10, 2026.
Source: UniClipboard/UniClipboard on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.