agentleFS
brilliantroughbrilliantrough/agent-skills/skills/accel/platform-environment-skill-audit/SKILL.md

platform-environment-skill-audit

Skill11 starsChanged 13 days agoMITView on GitHub

Audit accelerator environment skills and platform/cluster docs for personal repository, user, Conda, credential, and validation-artifact leakage; use when republishing CUDA, MUSA, PPU, ROCm, or other server environment guidance for multiple users.

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 5 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill before sharing a machine-specific accelerator skill or cluster runbook with another user or host.

    line 8
  2. Very specific:

    When auditing a specialized accel-platform-install / accel-pytorch-python / accel-pytorch-code-porting from the agent-skills repo, the …

    line 45
  3. Distinctive:

    Search recursively for old storage roots, usernames, hostnames, credentials, and copied validation dates.

    line 65
  4. Distinctive:

    Verify every advertised path exists on the intended host.

    line 68
  5. Distinctive:

    Remove secrets rather than masking only their display.

    line 72
  6. Distinctive:

    Do not rewrite a local skill into vague portable advice when the local facts are its purpose.

    line 75

≈ “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

Platform Environment Skill Audit

Use this skill before sharing a machine-specific accelerator skill or cluster runbook with another user or host.

Audit scope

Inspect the skill, every file it links to, and the commands it asks an agent to run. Separate portable guidance from local facts.

Search for:

  • personal usernames, initials, home directories, repositories, branches, and experiment names;
  • absolute data, checkpoint, result, cache, Conda, Toolkit, and runtime paths;
  • hostnames, IP addresses, ports, mount names, account names, and scheduler partitions;
  • credentials, tokens, proxy URLs, private package indexes, and shell history;
  • validation outputs that expose PIDs, job names, model names, or private data;
  • claims copied from another host without a current-host validation date.

Classification

Classify each fact as one of:

portable       valid for any supported installation
local-platform valid only for the named host or homogeneous host class
workload       belongs to one repository or experiment
sensitive      must not be published

Portable skills should use placeholders for user and storage roots. Local skills may name the host and paths, but must say so in the description and must not claim that another host has the same state. Workload details belong in the project, not in a platform skill.

Specialized accel-* trio (reflux path)

When auditing a specialized accel-platform-install / accel-pytorch-python / accel-pytorch-code-porting from the agent-skills repo, the platform layer is meant to be platform-generic but concrete:

  • Vendor-standard paths (e.g. /opt/musa, /usr/local/Ascend/..., /opt/maca, official versioned user-space prefixes) and platform version facts classify as portable — valid for any host of that platform following the same install. Do not demand placeholders for them; vagueness here is a defect, not safety.
  • Host-bound facts (user home dirs, this host's /data mounts and capacity, LVM/raw device names, hostname, IP, local-only tool paths) do not belong in the trio — move them to the host-layer skill (<host>-environment / system-profile style, named per host, never refluxed). That host-layer skill is the "clearly named local platform skill" this audit prescribes.
  • Reject the trio only for host-bound facts, credentials, or claims copied from another host without a current-host validation date — never merely for containing platform-standard absolute paths.

Required checks

  1. Search recursively for old storage roots, usernames, hostnames, credentials, and copied validation dates.
  2. Resolve symlinks and report broken or cross-user targets.
  3. Verify every advertised path exists on the intended host.
  4. Confirm package, driver, Toolkit, library, and Python versions live.
  5. Distinguish installation success, native linking, single-device compute, collectives, and application validation.
  6. Remove secrets rather than masking only their display.
  7. Re-run the search after editing and report remaining intentional local facts.

Do not rewrite a local skill into vague portable advice when the local facts are its purpose. Instead, keep a portable install skill and a clearly named local platform skill with explicit ownership boundaries.

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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.