Final Release Review
openai/openai-agents-python
Assess a Python SDK release candidate or release plan against the previous release and recommend ship or block.
A skill your agent uses when validating a MeshLLM release candidate or current HEAD against the last GitHub release, assembling the canonical feature/fix/modification inventory, testing locally…
$ npx skills add Mesh-LLM/mesh-llm --skill release-validation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Mesh-LLM/mesh-llm release-validation --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/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/release-validation .claude/skills/release-validation && 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 "release-validation" agent skill from https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/release-validation into .claude/skills/release-validation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "release-validation", 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/Mesh-LLM/mesh-llm/tree/main/.agents/skills/release-validationType 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 Mesh-LLM/mesh-llm --skill release-validation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Mesh-LLM/mesh-llm release-validation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/release-validation .agents/skills/release-validation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "release-validation" agent skill from https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/release-validation into .agents/skills/release-validation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "release-validation", 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 Mesh-LLM/mesh-llm --skill release-validation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Mesh-LLM/mesh-llm release-validation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/release-validation .cursor/skills/release-validation && 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 "release-validation" agent skill from https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/release-validation into .cursor/skills/release-validation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "release-validation", 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/Mesh-LLM/mesh-llm.git --path .agents/skills/release-validation--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 Mesh-LLM/mesh-llm --skill release-validation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Mesh-LLM/mesh-llm release-validation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/release-validation .gemini/skills/release-validation && 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 "release-validation" agent skill from https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/release-validation into .gemini/skills/release-validation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "release-validation", 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 Mesh-LLM/mesh-llm release-validationInstalls 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 Mesh-LLM/mesh-llm --skill release-validation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/release-validation .github/skills/release-validation && 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 "release-validation" agent skill from https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/release-validation into .github/skills/release-validation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "release-validation", 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 Mesh-LLM/mesh-llm --skill release-validation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Mesh-LLM/mesh-llm release-validation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/release-validation .opencode/skills/release-validation && 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 "release-validation" agent skill from https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/release-validation into .opencode/skills/release-validation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "release-validation", 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.
release-validationA skill your agent uses when validating a MeshLLM release candidate or current HEAD against the last GitHub release, assembling the canonical feature/fix/modification inventory, testing locally…
Release Validation is an agent skill from Mesh-LLM/mesh-llm. Use this skill when validating a MeshLLM release candidate or current HEAD against the last GitHub release, assembling the canonical feature/fix/modification inventory, testing locally built release bundles on user-approved real hosts and private meshes, deciding release readiness, or producing a formal evidence-backed release-validation report.
Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts, reference files and assets (for example `agents/openai.yaml`, `assets/release-validation-report-template.md` and `references/evidence-and-gates.md`).
It sits in Product & Project Management, covering Feature launches and release readiness. It works with GitHub. The repository describes itself as: Distributed AI/LLM for the people. Share compute privately or publicly to power your agents and chat. The licence is Apache-2.0.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 2b36552. 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.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
justFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
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.
Release Validation loads about 2.6k tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 1,308 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 no risky patterns in SKILL.md.
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); the scripts in this folder are not scanned.
The full file from Mesh-LLM/mesh-llm at commit 2b36552, republished under its Apache-2.0 licence (© Mesh-LLM). 1,308 words, ~2,622 tokens.
.claude/skills/release-validation/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.Validate the candidate as a product, not merely as source code. Every claimed change must have a disposition and direct evidence. Do not publish a release, push a tag, alter production, or use a host the user did not put in scope.
Read references/evidence-and-gates.md in full before planning or executing validation. Copy assets/release-validation-report-template.md for the final report; do not weaken or omit its required tables.
Resolve these before remote execution:
HEAD;Mesh-LLM/mesh-llm;target/release-validation/<UTC timestamp>-<short SHA>/;If host selection, access, or cost authority is missing, complete the read-only inventory and test plan, then stop before remote build or launch.
Read each applicable skill completely before acting:
.agents/skills/remote-observable-process/SKILL.md for every long-running or
interactive SSH build/server session;.agents/skills/deploy-macos/SKILL.md,
.agents/skills/deploy-linux-gpu/SKILL.md, or
.agents/skills/deploy-windows/SKILL.md for the selected host platform;.agents/skills/mesh-join/SKILL.md for private-mesh creation and verification;.agents/skills/connect-agents/SKILL.md for agent/tool-call validation;.agents/skills/manage-ci/SKILL.md before inspecting CI, release workflows,
runners, artifacts, or live workflow results;origin/main. An off-main candidate must be passed as its explicit release
tag or have exactly one local tag pointing at its SHA; use that canonical tag
for the subject check and fail closed if it is absent or ambiguous. Allow
zero commits above a release base, or exactly one commit whose subject is the
tag-specific <tag>: prepare release source. Require the previous-release
base to be an ancestor of the candidate base. Fail closed if origin/main is
unavailable or any tag, subject, commit-count, or base ordering check fails.scripts/collect-release-inventory.py from this skill to capture a raw
JSON evidence manifest. If its exact release tag is missing locally, verify
the configured remote URL and fetch that tag before rerunning. The script
does not classify changes.Use all of these sources, not release notes or commit subjects alone:
Create one atomic ledger row per externally meaningful claim. Merge duplicate
PRs/commits into one item, but split unrelated behavior hidden in one PR.
Classify every row as FEATURE, BUG_FIX, or REVISION:
FEATURE: a newly available user/operator/developer capability;BUG_FIX: behavior that now satisfies an existing contract or removes a
defect/regression;REVISION: changed semantics, UX, performance, dependency, packaging,
protocol, docs, or operational behavior that is neither of the above.Give each row a stable ID (RV-FEAT-###, RV-FIX-###, or RV-REV-###), a
precise claim, source PRs/commits/files, affected surfaces, compatibility and
risk notes, and at least one positive and one relevant negative/edge test.
Record internal-only changes as revisions when they can affect release risk;
otherwise list them in the excluded/non-release-impact appendix with rationale.
Map every ledger item to concrete checks and an evidence destination. Cover
the common release matrix in the evidence reference plus all change-specific
paths. Mark a test NOT_APPLICABLE only with a written reason. UNVERIFIED is
not a pass.
Use risk to order work: provenance and packaging first, startup/readiness next, then APIs/logs/UI/inference, feature claims, failure/recovery, mixed-version compatibility, and nonfunctional checks. Do not allow one smoke test to stand in for multiple materially different claims.
Confirm each host identity, OS/architecture, backend, GPU/driver/runtime, free disk/RAM/VRAM, toolchain, ports, and existing MeshLLM processes.
Transfer or check out the exact candidate source. Verify the candidate SHA on every host before building.
Use just; never invoke ad hoc Cargo builds as release evidence. Build the
three-layer product with the applicable canonical recipes:
just release-host-build
just release-runtime-build <backend>
just release-bundle <candidate-version> <output-directory>Use the Windows/platform-specific recipes where the Justfile requires them.
Record commands, exit codes, duration, output archive names and SHA-256,
host-import policy results, product/runtime manifests, ABI/version metadata,
binary version, and archive contents. Run just check-release and any
applicable consistency checks.
Execute the extracted packaged object. Do not validate only
target/release/mesh-llm, and do not substitute an older downloaded runtime.
Use at least two user-approved real hosts when available. Start foreground, observable processes with JSON logging and isolated ports/data/runtime state. Create a private mesh on one candidate bundle, join the other candidate bundle with its invite token, and wait for explicit readiness rather than sleeping a fixed interval.
Prove on both nodes:
/api/status and relevant management APIs;/v1/models union and local/remote model identity;auto;mesh and tool-call behavior when supported or affected;For wire, gossip, routing, discovery, packaging, or compatibility changes, add a separate mixed-version private-mesh check using the last released packaged binary on one host and the candidate on another. Never replace the all-candidate mesh with this compatibility check.
Assign exactly one status to each ledger item:
PASS: the claim is complete and directly proven in every required scope;FAIL: observed behavior contradicts the claim or creates a release blocker;PARTIAL: part works, but the claim, platform matrix, UX, docs, or recovery
behavior is incomplete;BLOCKED: validation could not run because a named prerequisite is missing;NOT_APPLICABLE: a planned dimension truly does not apply, with rationale;UNVERIFIED: no adequate evidence was obtained.Link immutable or locally preserved evidence: commands with exit codes, JSON
responses, redacted log excerpts, screenshots, checksums, manifests, test
outputs, and defect references. Never infer PASS from code inspection alone.
Write release-validation-report.md from the bundled template and store raw
evidence beside it. Include an evidence index with relative paths. Redact
tokens, credentials, private addresses when required, and customer data.
Apply the gate rules from the evidence reference. Give one decision:
READY, CONDITIONALLY_READY, NOT_READY, or INCOMPLETE. A conditional
decision requires an explicit waiver owner, rationale, expiry, and bounded
residual risk. Do not call a release ready while any required row is failed,
partial, blocked, or unverified.
context/COMPUTERS.md;
never invent a host or use a raw IP when an alias exists.© Mesh-LLM, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 4 other files (scripts, references, assets) in .agents/skills/release-validation of Mesh-LLM/mesh-llm.
Open the folder on GitHubat commit 2b36552
Release Validation 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 |
|---|---|---|---|---|---|---|
| Release Validation this skillMesh-LLM/mesh-llm | 3.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Final Release Reviewopenai/openai-agents-python | 30k | — | ~5.4k | Automated safety check: Pass | MIT | |
| Create Release Checklistquarto-dev/quarto-r | 160 | 1 repos | ~1.9k | Automated safety check: Notes | MIT | |
| Dogfoodpaiml/aprender | 127 | — | ~13k | Automated safety check: Pass | MIT | |
| Release Readiness Reviewkernitus/BukkitOldCombatMechanics | 223 | — | ~1.5k | Automated safety check: Pass | MPL-2.0 | |
| Analyzing Release Readinessaws/agent-toolkit-for-aws | 2.8k | — | ~5k | Automated safety check: Pass | Apache-2.0 |
openai/openai-agents-python
Assess a Python SDK release candidate or release plan against the previous release and recommend ship or block.
quarto-dev/quarto-r
Create a release checklist and GitHub issue for an R package.
paiml/aprender
Sovereign-stack PRE-RELEASE protocol. An agent skill from paiml/aprender.
kernitus/BukkitOldCombatMechanics
A skill your agent uses for GitHub release, Hangar, CurseForge/BukkitDev upload, Spigot release handoff, licence, asset naming, supported-version, and workflow readiness checks; do not use for…
aws/agent-toolkit-for-aws
Trigger a pre-merge release readiness review on a GitHub PR, GitLab MR, or local branch.
serithemage/serverless-openclaw
Runs solo-maintainer release work end-to-end: release readiness review, notes, tags, GitHub release creation, deploy workflow dispatch, and post-release verification.
Mesh-LLM/mesh-llm
A skill your agent uses when running, debugging, interpreting, or documenting mesh-llm benchmark tune model-serving throughput trials, including choosing…
Mesh-LLM/mesh-llm
A skill your agent uses when adding, renaming, removing, validating, or exposing mesh-llm config settings, including built-in settings, plugin config schemas, owner-control apply behavior, CLI…
Mesh-LLM/mesh-llm
A skill your agent uses when connecting agent tools or OpenAI clients to mesh-llm — launching or configuring Goose, Claude Code, OpenCode, Pi, curl, or any OpenAI-compatible client against a local…
Mesh-LLM/mesh-llm
A skill your agent uses when converting Hugging Face SafeTensors checkpoints into split BF16 GGUF model repos with skippy-quantize on Hugging Face Jobs or a local machine, then publishing the…
Mesh-LLM/mesh-llm
A skill your agent uses when creating, monitoring, validating, or documenting low-memory Hugging Face Jobs or local runs that quantize split BF16/FP16 GGUF model repos into custom quant GGUF repos…
Mesh-LLM/mesh-llm
A skill your agent uses when changing mesh-llm automation or CLI flows that discover Hugging Face GGUF models, plan CPU Hugging Face Jobs for layer-package splitting, estimate max cost, or publish…
Works with
Categories
A skill your agent uses when validating a MeshLLM release candidate or current HEAD against the last GitHub release, assembling the canonical feature/fix/modification inventory, testing locally…. Release Validation is an agent skill from Mesh-LLM/mesh-llm. Use this skill when validating a MeshLLM release candidate or current HEAD against the last GitHub release, assembling the canonical feature/fix/modification inventory, testing locally built release bundles on user-approved real hosts and private meshes, deciding release readiness, or producing a formal evidence-backed release-validation report.
Release Validation fits situations like: validating a MeshLLM release candidate; current HEAD against the last GitHub release; assembling the canonical feature/fix/modification inventory; testing locally built release bundles on user-approved real hosts and private meshes.
Run `npx skills add Mesh-LLM/mesh-llm --skill release-validation -a claude-code`. Or copy the skill folder (.agents/skills/release-validation in Mesh-LLM/mesh-llm) into .claude/skills/release-validation in your project. Claude Code loads it when a task matches its description.
Run `npx skills add Mesh-LLM/mesh-llm --skill release-validation -a codex`. Or copy the skill folder (.agents/skills/release-validation in Mesh-LLM/mesh-llm) into .agents/skills/release-validation 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 Mesh-LLM/mesh-llm --skill release-validation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/release-validation, .gemini/skills/release-validation, .github/skills/release-validation and .opencode/skills/release-validation in your project.
Going by SKILL.md and its folder, Release Validation needs Python for the scripts in its folder and the command-line tools its instructions call (just). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Release Validation is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.9k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Release Validation: Final Release Review (openai/openai-agents-python, 30k stars), Create Release Checklist (quarto-dev/quarto-r, 160 stars), Dogfood (paiml/aprender, 127 stars) and Release Readiness Review (kernitus/BukkitOldCombatMechanics, 223 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Mesh-LLM (a GitHub organization) maintains it in Mesh-LLM/mesh-llm, which has 3,484 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on October 7, 2026.
Source: Mesh-LLM/mesh-llm on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.