agentleFS
carlkiblercarlkibler/agent-skills/skills/remote-host-verifier/SKILL.md

remote-host-verifier

Skill6 starsChanged 4 months agoMITView on GitHub

Compare commands, versions, config, and behavior across local and SSH hosts.

  • Reads credentials

The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 4 are distinctive, and 5 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use when "works on my machine" is not enough.

    line 15
  2. Distinctive:

    Run safe checks first:

    line 40
  3. Distinctive:

    Look for time skew, nearly-full disks, different shells, and PATH order changes.

    line 58
  4. Distinctive:

    Run the smallest equivalent command locally and remotely.

    line 62
  5. Very specific:

    If release/install/update was the goal, finish with:

    line 93
  6. Very specific:

    Use scripts/remote_compare.py to run the same command locally and on one or more SSH hosts and emit text or JSON.

    line 107
  7. Very specific:

    Use release-operator before publishing a CLI that must update on remote hosts.

    line 113
  8. Very specific:

    Use agent-log-forensics when the remote host has the richer agent logs to scan.

    line 114
  9. Very specific:

    Use chezmoi-drift when environment parity points at dotfile or shared-skill drift.

    line 115

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Remote Host Verifier

Use when "works on my machine" is not enough.

Third-order stance: do not stop at whether the command works now. Ask whether this host will diverge, decay, or betray the next agent tomorrow because PATH, time, shell startup, package source, disk, or dotfiles are drifting.

When to Use

  • The user names a remote host (an SSH alias such as devbox)
  • A CLI install/update/release must work on more than one machine
  • You need to compare local vs remote config, version, path, or behavior
  • A remote machine has more representative data than the current one
  • The task depends on environment parity, time sync, package provenance, or user-level dotfiles

When NOT to Use

  • The command is destructive and no dry-run mode exists
  • The user hasn't said which hosts you may connect to (don't pick hosts from ~/.ssh/config yourself)
  • The user did not name or imply remote validation
  • The result depends on GUI state you cannot inspect over SSH

<process>

Phase 1: Verify Connectivity and Identity

Run safe checks first:

hostname
command -v <tool>
<tool> --version
ssh <host> 'hostname; command -v <tool>; <tool> --version'

Capture exact hostnames. Do not assume an SSH alias points where it did last time. Use non-interactive SSH (ssh -o BatchMode=yes <host> ...) so a password or host-key prompt fails fast instead of hanging; if it fails, report the connection error rather than guessing.

Also capture environment risk signals before trusting the comparison:

ssh <host> 'date -u "+%Y-%m-%dT%H:%M:%SZ"; uptime; df -h .; echo "$SHELL"; printf "%s\n" "$PATH"'
date -u "+%Y-%m-%dT%H:%M:%SZ"; uptime; df -h .; echo "$SHELL"; printf "%s\n" "$PATH"

Look for time skew, nearly-full disks, different shells, and PATH order changes. These are predictive failures: the current command may pass while the next release/update fails.

Phase 2: Compare the Same Command

Run the smallest equivalent command locally and remotely. Prefer JSON output where available.

# path relative to this skill's directory; add --probe-env for time/PATH/disk probes
python3 scripts/remote_compare.py --host <host> --probe-env -- <tool> doctor --smoke --json

For a mutating command, use a documented read-only or dry-run mode, or an authorized disposable workspace. A --json flag changes output format and does not establish read-only behavior.

Phase 3: Normalize and Interpret

Strip terminal noise and compare:

  • version
  • binary path
  • config path
  • installed package source
  • command output
  • exit status
  • notable stderr
  • time, PATH, shell, disk, and package-manager drift

Classify differences as:

  • Expected — hostname, local paths, data volume
  • Action needed — old version, missing command, config points at stale checkout
  • Unknown — needs another command to explain
  • Predictive failure — works now but environmental drift makes the next command risky

Phase 4: Close the Loop

If release/install/update was the goal, finish with:

<tool> --version
<tool> doctor --smoke
ssh <host> '<tool> --version; <tool> doctor --smoke'

Report only after both sides have evidence. Finish with a verdict per host (PASS / FAIL / INCONCLUSIVE), the exact commands run, the differences classified as above, and a small "tomorrow risk" note if drift is present even when the command passes. Stop once each named host has a verdict; don't widen to other hosts.

</process><scripts>

Use scripts/remote_compare.py to run the same command locally and on one or more SSH hosts and emit text or JSON. Add --probe-env to include time, shell, PATH, cwd, disk, and uptime probes.

</scripts><interlocks>
  • Use release-operator before publishing a CLI that must update on remote hosts.
  • Use agent-log-forensics when the remote host has the richer agent logs to scan.
  • Use chezmoi-drift when environment parity points at dotfile or shared-skill drift.
</interlocks>

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.