Agent skill

Release Validation

by Mesh-LLM in Mesh-LLM/mesh-llm

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…

Apache-2.0Auto-check passedProduct & Project Management

Install Release Validation

skills CLI
$ npx skills add Mesh-LLM/mesh-llm --skill release-validation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Mesh-LLM/mesh-llm release-validation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
release-validation
GitHub stars
3.5k
Token cost
~2.6k tokens
SKILL.md length
1,308 words
Files
5 (incl. scripts, references, assets)
Skills in repo
25
Repo updated
First seen
Licence
Apache-2.0

At a glance

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…

  • Works in 7 steps: Freeze Provenance → Build The Canonical Delta Ledger → Design Tests Before Building → …
  • Validating a MeshLLM release candidate
  • SKILL.md covers Required Inputs, Route To Existing Skills, Workflow and Operating Rules
  • Runs Python scripts from its folder; calls just

What it does

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.

When your agent uses it

  • 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

Example prompts

  • “/release-validation”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Freeze Provenance
  2. Build The Canonical Delta Ledger
  3. Design Tests Before Building
  4. Build Canonical Products On Real Hosts
  5. Exercise A Private Mesh
  6. Judge Every Claim
  7. Produce The Formal Report

What it can do on your machine

Read from SKILL.md and the folder at commit 2b36552. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • just

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~92
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.5k

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.

Safety

Auto-check passed

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.

SKILL.md

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.

Download SKILL.mdSave it as .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.
name
release-validation
description
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

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.

Required Inputs

Resolve these before remote execution:

  • candidate ref, defaulting to the current HEAD;
  • repository, normally Mesh-LLM/mesh-llm;
  • exact user-approved SSH aliases or local hosts, platform/backend on each, and any cost or time limits;
  • models suitable for each host and any feature-specific fixtures;
  • output directory, defaulting to target/release-validation/<UTC timestamp>-<short SHA>/;
  • whether a previous-release mixed-version node is authorized when compatibility testing is required.

If host selection, access, or cost authority is missing, complete the read-only inventory and test plan, then stop before remote build or launch.

Route To Existing Skills

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;
  • the relevant Skippy, plugin, telemetry, configuration, or benchmark skill when the delta touches that subsystem.

Workflow

1. Freeze Provenance
  1. Record the candidate SHA, branch, remotes, submodule state, dirty paths, toolchain versions, and current UTC time.
  2. Refuse to present dirty or mismatched builds as reproducible. Either obtain explicit approval to validate the exact dirty tree and record its diff hash, or use a clean checkout at the candidate SHA.
  3. Inspect the latest published GitHub release, its tag, notes, assets, checksums, publication time, and release workflow conclusion. Distinguish stable, prerelease, draft, and manually superseded releases.
  4. Before collecting inventory, resolve the candidate SHA and previous-release tag commit and derive each release base from its merge-base with 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.
  5. Run 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.
2. Build The Canonical Delta Ledger

Use all of these sources, not release notes or commit subjects alone:

  • GitHub comparison and merged PRs from the release tag through candidate SHA;
  • local commit history and changed-file diff;
  • PR bodies, linked issues, labels, tests, docs, migrations, and generated artifacts;
  • user-visible CLI/API/UI/config/protocol behavior found in the code;
  • release, installer, packaging, dependency, security, and observability changes.

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.

3. Design Tests Before Building

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.

4. Build Canonical Products On Real Hosts
  1. Confirm each host identity, OS/architecture, backend, GPU/driver/runtime, free disk/RAM/VRAM, toolchain, ports, and existing MeshLLM processes.

  2. Transfer or check out the exact candidate source. Verify the candidate SHA on every host before building.

  3. Use just; never invoke ad hoc Cargo builds as release evidence. Build the three-layer product with the applicable canonical recipes:

    bash
    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.

  4. 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.

  5. Execute the extracted packaged object. Do not validate only target/release/mesh-llm, and do not substitute an older downloaded runtime.

Show full SKILL.md (485 more words)Show less
5. Exercise A Private Mesh

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:

  • peer membership and stable readiness;
  • /api/status and relevant management APIs;
  • /v1/models union and local/remote model identity;
  • non-streaming and streaming inference through exact model IDs and auto;
  • mesh and tool-call behavior when supported or affected;
  • structured logs parse as JSON, contain expected lifecycle/routing events, and contain no panic, secret, unexplained retry storm, or hidden fatal error;
  • embedded UI loads, reflects the same state, has no blocking console/network errors, and completes the item-specific interactions;
  • graceful stop, peer loss/recovery, restart, and cleanup.

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.

6. Judge Every Claim

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.

7. Produce The Formal Report

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.

Operating Rules

  • Preserve unrelated worktree and host state. Use isolated directories, ports, and process identifiers; stop only processes created by this run.
  • Use SSH aliases supplied by the user or documented in context/COMPUTERS.md; never invent a host or use a raw IP when an alias exists.
  • Do not expose invite tokens, API keys, owner keys, release signing material, private paths, or unredacted logs in the report.
  • Do not fix discovered product defects during validation unless the user separately authorizes implementation. Record a reproducible defect and its release impact.
  • Do not dispatch/cancel CI, publish artifacts, push commits/tags, create a release, or mutate GitHub configuration without explicit authorization.
  • Report ongoing host cost and stop remote processes promptly when evidence is complete or the run is blocked.

© 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

Files

SKILL.md and 4 other files (scripts, references, assets) in .agents/skills/release-validation of Mesh-LLM/mesh-llm.

  • SKILL.md
  • agents/openai.yaml
  • assets/release-validation-report-template.md
  • references/evidence-and-gates.md
  • scripts/collect-release-inventory.py

Open the folder on GitHubat commit 2b36552

Compare with similar skills

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.

Release Validation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Release Validation this skillMesh-LLM/mesh-llm3.5k—~2.6kAutomated safety check: PassApache-2.0
Final Release Reviewopenai/openai-agents-python30k—~5.4kAutomated safety check: PassMIT
Create Release Checklistquarto-dev/quarto-r1601 repos~1.9kAutomated safety check: NotesMIT
Dogfoodpaiml/aprender127—~13kAutomated safety check: PassMIT
Release Readiness Reviewkernitus/BukkitOldCombatMechanics223—~1.5kAutomated safety check: PassMPL-2.0
Analyzing Release Readinessaws/agent-toolkit-for-aws2.8k—~5kAutomated safety check: PassApache-2.0

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Works with

Questions about Release Validation

What does Release Validation do?

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.

When should I use Release Validation?

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.

How do I install Release Validation in Claude Code?

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.

How do I install Release Validation in Codex?

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.

Can I use Release Validation in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Release Validation need to run?

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.

Does Release Validation access the network?

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.

Is Release Validation safe to install?

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.

What licence does Release Validation use?

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.

How many tokens does Release Validation use?

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.

What are the alternatives to Release Validation?

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.

Who maintains Release Validation?

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.