Agent skill

Release Sample Sweep

by Atmosphere in Atmosphere/atmosphere

Run the pre-release end-to-end sweep of every user-facing surface — the 33 samples under samples/ (booted from their packaged artifacts and driven in a real browser via chrome-devtools MCP), the…

Apache-2.0Auto-check passedMobile

Install Release Sample Sweep

skills CLI
$ npx skills add Atmosphere/atmosphere --skill release-sample-sweep -a claude-code

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

GitHub CLI
$ gh skill install Atmosphere/atmosphere release-sample-sweep --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/Atmosphere/atmosphere.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/release-sample-sweep .claude/skills/release-sample-sweep && 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-sample-sweep
GitHub stars
3.8k
Token cost
~4.2k tokens
SKILL.md length
1,972 words
Files
10 (incl. scripts, references, assets)
Skills in repo
20
Repo updated
First seen
Licence
Apache-2.0

At a glance

Run the pre-release end-to-end sweep of every user-facing surface — the 33 samples under samples/ (booted from their packaged artifacts and driven in a real browser via chrome-devtools MCP), the…

  • Works in 5 steps: Preconditions → Triage → Fix, with a regression test per issue → …
  • Tasks that involve Cross-platform mobile apps
  • SKILL.md covers When to run it, What it catches that CI does not, The shape of the sweep and Non-negotiables, plus 9 more sections
  • Runs Shell scripts from its folder; calls git, ollama and curl; needs LLM_API_KEY

What it does

Release Sample Sweep is an agent skill from Atmosphere/atmosphere. Run the pre-release end-to-end sweep of every user-facing surface — the 33 samples under samples/ (booted from their packaged artifacts and driven in a real browser via chrome-devtools MCP), the Expo/React Native client, and the atmosphere CLI. Use before cutting a release, and after any change to the Console bundle, a shared module, atmosphere.js, the CLI, or several samples at once. Covers preconditions, the keyless Ollama backend, the per-sample launch/drive/collect/teardown loop, the evidence ledger, the fix…

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts, reference files and assets (for example `assets/ledger-template.md`, `references/cli-sweep.md` and `references/driving-recipes.md`).

It sits in Mobile, covering Cross-platform mobile apps, Browser testing and LLM inference and serving. It works with Chrome DevTools, Model Context Protocol, Expo and Ollama. The repository describes itself as: Portable AI agent runtime for the JVM. One @Agent class runs on Spring AI, LangChain4j, Anthropic, or 9 more behind one SPI. Token streaming, tool calls, human approvals, and… The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Cross-platform mobile apps
  • Tasks that involve Browser testing
  • Tasks that involve LLM inference and serving

Example prompts

  • “/release-sample-sweep”

Requirements

  • A Bash shell
  • Docker
  • A credential in LLM_API_KEY

Workflow steps

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

  1. Preconditions
  2. Triage
  3. Fix, with a regression test per issue
  4. Re-test
  5. Report

What it can do on your machine

Read from SKILL.md and the folder at commit 13671cf. 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/ (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • git
    • ollama
    • curl
    • java
    • claude

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

  • Network

    No URLs in SKILL.md. Its commands use git and curl, which can reach the network depending on how they are called.

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • LLM_API_KEY

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

Context cost

Release Sample Sweep loads about 4.2k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 161 tokens; SKILL.md has 1,972 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~161
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~19k

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 Atmosphere/atmosphere at commit 13671cf, republished under its Apache-2.0 licence (© Atmosphere). 1,972 words, ~4,191 tokens.

Download SKILL.mdSave it as .claude/skills/release-sample-sweep/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
release-sample-sweep
description
Run the pre-release end-to-end sweep of every user-facing surface — the 33 samples under samples/ (booted from their packaged artifacts and driven in a real browser via chrome-devtools MCP), the Expo/React Native client, and the atmosphere CLI. Use before cutting a release, and after any change to the Console bundle, a shared module, atmosphere.js, the CLI, or several samples at once. Covers preconditions, the keyless Ollama backend, the per-sample launch/drive/collect/teardown loop, the evidence ledger, the fix phase (every issue gets a biting regression test in the right suite), the re-test subset, and the report.

Release sample sweep (chrome-devtools)

Before a release, every user-facing surface is exercised the way a user exercises it. This skill is that procedure written down.

Three surfaces, three drivers — all three are release gates:

SurfaceWhatDriver
Samples (33)samples/*, booted from packaged artifactschrome-devtools MCP, or the wire protocol for the headless ones
Expo client (1)samples/spring-boot-ai-classroom/expo-client/iOS simulator MCP — it is a native app, chrome-devtools cannot reach it
CLIatmosphere run / new / compose / import / checkpoint + its four distributionsShell, then chrome-devtools against what atmosphere run booted

When to run it

  • Before cutting any release. Non-negotiable — it is the last gate before release-4x.yml.
  • After a change to the Console bundle (modules/spring-boot-starter/frontend/), since the Console is both the shipped sample UI and the validation surface.
  • After a change to a shared module that every sample transitively depends on (modules/cpr, modules/ai, modules/spring-boot-starter, modules/admin).
  • After a dependency bump wave — two of the last three sweeps found a version-skew bug that compiled clean and only failed at runtime.

What it catches that CI does not

CI builds and tests modules; this sweep exercises packaged artifacts in a browser. The gap between those is where the real bugs live:

SweepBug foundWhy CI was green
2026-06-30quarkus-ai-chat would not start — OTel api/common version skew from a Dependabot bumpModule tests never boot the sample's fast-jar
2026-07-17spring-boot-orchestration-demo crashed on every tool turn — the sample pom hardcoded langchain4j-open-ai:1.15.0 while the reactor manages 1.17.0The module built against 1.17.0; only the sample's own jar bundled 1.15.0

scripts/release-gate-samples.sh automates the boot-and-assert half of this in CI. This sweep is the browser half on top of it — the layer that sees rendering, streaming, transport headers, tool cards, and console errors.

The shape of the sweep

Step 0   Preconditions  — build everything, start Ollama, free the ports, open the ledger
Step 1a  Samples        — 33 samples: launch → drive → collect → verdict → teardown
Step 1b  Expo client    — the RN client in the iOS simulator
Step 1c  CLI            — atmosphere run/new/compose/import/checkpoint + distributions
                           ALL OF PHASE 1 IS COLLECT-ONLY. Do not fix anything mid-sweep.
Step 2   Triage         — classify every finding, rank by blast radius
Step 3   Fix            — root-cause fix + a regression test per issue, in the right
                           suite, each proven to bite
Step 4   Re-test        — the failed surfaces in full, plus the blast-radius subset
                           of already-passing ones
Step 5   Report         — vault report, CI green, memory updated

Step 1 is deliberately fix-free. Fixing mid-sweep changes the artifact under test and invalidates every sample already verified against the old one. The one exception: a defect that blocks the sweep itself from continuing — fix it, say so in the ledger, and note which already-passed samples were re-run.

Non-negotiables

  1. chrome-devtools, never curl, for validation. curl is allowed only for port readiness and for headless wire protocols (A2A/MCP/REST) that serve no HTML. A "works via curl" claim skips the whole JS layer and is a false pass.
  2. The Atmosphere Console is the UI. Drive /atmosphere/console/ (Spring Boot samples redirect / there). A sample that needs a bespoke page instead of the Console is itself a finding.
  3. Assert the rendered element, not the payload. An image node with a src is a rendered screenshot; the same base64 in a StaticText node means nothing rendered it. "Server started", "HTTP 200", and "bytes present in the DOM" are not passes.
  4. Boot the packaged artifact. java -jar (or quarkus-run.jar), never spring-boot:run / quarkus:dev. Both historical bugs above existed only at artifact level.
  5. Kill by PID, never pkill -f. Never touch a port or process the sweep did not start — if a port is occupied, move to another port. The same rule covers the machine's network: never run networksetup, never take an interface down, never touch VPN/DNS/proxy settings. The host's Wi-Fi carries every session the maintainer has open, and a sweep interrupted mid-toggle can leave the machine offline indefinitely. Any assertion that needs real network loss is recorded PARTIAL with its unit coverage cited — see references/expo-sweep.md.
  6. Model limitation ≠ framework bug. A small local model emitting invalid tool-call arguments is a model limitation; record it as such and prove it by re-running the same flow on a capable model before calling it a regression.
  7. Never write "flaky". Reproduce it, or explain the mechanism. If neither is possible yet, it is a FAIL with an open question, not a dismissal.
  8. Report honestly. PASS / PARTIAL / FAIL with one line of concrete evidence each. PARTIAL must name what was not proven and why.

Step 0 — Preconditions

bash
git status --porcelain                    # must be clean
git rev-parse --short HEAD                # record this SHA in the ledger
grep -m1 '<version>' pom.xml              # record the version under test

./mvnw install -DskipTests -Pfastinstall  # full reactor: framework + every sample jar
./scripts/sync-console-bundle.sh --check  # the Console you will drive must be current

ollama list                               # qwen2.5:3b + qwen2.5:7b-instruct-q4_K_M
curl -s -o /dev/null -w '%{http_code}\n' http://localhost:11434/v1/models
  • LLM backend is local Ollama, keyless. Use qwen2.5:3b for streaming samples and qwen2.5:7b-instruct-q4_K_M for tool-heavy agents — 3b emits invalid tool-call arguments and Ollama answers 400. Note real-ollama is a CI-harness alias only; AiConfig matches the literal local.

  • The launcher scrubs ambient LLM env (LLM_API_KEY, LLM_BASE_URL, LLM_MODE, LLM_MODEL, and the provider keys) from every sample it boots, so the sweep is reproducible on any machine. SWEEP_KEEP_ENV=1 inherits instead. If you boot a sample by hand, scrub them yourself — a maintainer's profile routinely exports these.

  • Always read the resolved endpoint out of the boot log before driving:

    grep 'AI config:' target/sweep/<sample>.log

    Expect mode=local … endpoint=http://localhost:11434/v1. Anything else means the sample is not talking to Ollama and the turn's result says nothing about this build. An explicit LLM_BASE_URL outranks the mode by design, so an inherited one silently redirects a "local" run to a remote provider — that is what happened on the 2026-08-07 shakedown before the scrub existed.

  • Do not use a paid key. The paid-LLM lane is retired; quota starvation is what made the 2026-06 sweep report nine samples as plumbing-only.

  • Do not use embacle (embacle-server --provider claude_code) for tool-calling samples — it applies the host CLI's own configuration to responses and its tool-call fidelity is inconsistent. It is only useful to demonstrate "a capable model completes this flow cleanly", then stop it.

  • Ports: the sweep runs on the 9101+ block so it never collides with the samples' own defaults or the Playwright fixture's 8080–8104. Assignments are in references/sample-matrix.md.

  • Open the ledger at claude_docs/sample-sweep-<YYYY-MM-DD>.md (a gitignored symlink into the vault, so it survives context compaction). Template: assets/ledger-template.md. Write each row as you finish that sample, never in a batch at the end.

Step 1a — The per-sample loop

Work through references/sample-matrix.md in order. For each sample:

bash
# 1. Launch (the helper refuses to boot if the port is already answering)
.claude/skills/release-sample-sweep/scripts/sweep-sample.sh start <sample> \
    --port <9101+n> --ready-path <path> --env LLM_MODE=local --env LLM_MODEL=qwen2.5:3b
  1. Fresh browser page per sample — new_page on the drive URL. Never reuse the previous sample's page: stale state and leftover console noise both corrupt the evidence.
  2. Snapshot — take_snapshot. Confirm the Console mounted and the transport badge reads what the matrix expects (Connected · websocket / · webtransport / · grpc / · ag-ui). A transport that silently fell back is a finding.
  3. Drive the headline flow for that sample — the exact interaction is in the matrix, the mechanics per surface class are in references/driving-recipes.md.
  4. Wait for the rendered result — wait_for the expected text/element, then re-take_snapshot and confirm the node type (see non-negotiable #3).
  5. Collect the evidence, all three sources:
    • list_console_messages — every error and warning, verbatim
    • list_network_requests — any non-2xx/failed request
    • sweep-sample.sh warnings <sample> — server-side WARN/ERROR/exception/SLF4J Record warnings even when the sample passes. The warning inventory is half the value of the sweep and is what the next release's triage starts from.
  6. Verdict + one-line evidence into the ledger:
    • PASS — headline feature observed rendered, no unexplained console error, no server exception.
    • PARTIAL — plumbing proven, headline feature not observed, reason named (missing third-party key, Docker unavailable, model limitation).
    • FAIL — feature broken, error frame, exception, or the sample won't boot.
  7. Teardown — close_page, then sweep-sample.sh stop <sample>. The helper verifies the port is actually released; if it is not, stop and investigate before the next sample claims it.
Show full SKILL.md (823 more words)Show less

Step 1b — The Expo client

samples/spring-boot-ai-classroom/expo-client/ is a native Expo/RN app. It is not a Maven module, not in cli/samples.json, and unreachable by the Playwright suites — this sweep is its only gate. It links atmosphere.js by file path, so it is also the only pre-release check that the client library's ./react-native export works in a real RN runtime.

Driven with the iOS simulator MCP, not chrome-devtools. Full procedure, including the SERVER_URL port trap and the AppState/NetInfo assertions nothing else covers: references/expo-sweep.md.

Step 1c — The CLI

The CLI is the documented Quick Start and ships as four distributions (curl installer, npx, Homebrew tap, SDKMAN). CI covers list/info, argument validation, the runtime overlays, and the installers — it never boots a sample through atmosphere run and looks at the UI, and it has no coverage for compose or checkpoint.

The manual pass closes that: atmosphere run → browser-driven, atmosphere new → scaffold + compile against Maven Central, plus the thin-coverage commands and a post-publish check of the actually-shipped artifacts. Watch the jar cache — a stale $ATMOSPHERE_HOME/cache/v<version> boots the previous release and fakes a pass. Full procedure: references/cli-sweep.md.

Step 2 — Triage

With all samples tested, classify each finding before touching any code:

ClassMeaningAction
Framework bugA module under modules/ is wrongFix + regression spec. Release-blocking.
Sample bugOnly that sample's code/pom/config is wrongFix + regression spec. Release-blocking if the sample ships.
Config/envSample needs a key, Docker, a collectorNot a bug — document the graceful-degradation behaviour and assert that
Model limitationSmall local model can't drive the flowProve with a capable model, record, no code change
Sweep environmentPort conflict, stale ~/.m2, half-built reactorFix the environment and re-run that sample

Rank by blast radius: shared-module findings first (they can invalidate other samples' passes), then per-sample.

Step 3 — Fix, with a regression test per issue

Every issue gets a test, but in the suite that can actually run it:

Surface the issue is onRegression home
Sample / Console / frameworkPlaywright spec → references/regression-specs.md
CLIA case in cli/test-cli.sh (a Playwright spec is the wrong vehicle for a shell CLI)
Expo / React NativeAn atmosphere.js vitest covering the ./react-native export path; if the defect is genuinely RN-runtime-only, name it in the report as manual-sweep-only rather than faking a gate

For every issue in the framework-bug or sample-bug class:

  1. Root-cause it first. Read the failing path; do not pattern-match a fix.
  2. Smallest change that fixes the cause — the 2026-07 langchain4j fix was a single pom property.
  3. Write a Playwright e2e spec that reproduces the failure, in the right home, wired into the right CI lane, and proven to bite: it must fail against the pre-fix artifact and pass after. Recording only "it passes now" proves nothing. Full authoring + wiring recipe: references/regression-specs.md.
  4. Where a build-time lint can close the whole class, add that too — the langchain4j fix shipped both a spec and SampleLangChain4jVersionLintTest, which fails the build if any sample pom hardcodes a LangChain4j version.
  5. Rebuild the affected modules and samples before re-testing.
  6. One commit per fix class, conventional-commit prefixed. No CHANGELOG edits — the CHANGELOG is touched only at release time.

Step 4 — Re-test

  1. Re-run the failed sample end to end — the full headline flow, not just the broken step.
  2. Re-run the blast-radius subset of already-passing samples. The fix changed the artifact those passes were recorded against, so their evidence is only still valid if the fix could not reach them. The mapping from "what the fix touched" to "which passing samples must be re-driven" is in references/retest-subset.md.
  3. Say what you did not re-run and why. A subset is a deliberate scope decision; leaving it unstated reads as "everything was re-verified".
  4. Repeat triage → fix → re-test until the sweep is clean.

Step 5 — Report

  • Vault report via the obsidian-writer skill → Claude Outputs/Sample-Sweep-chrome-devtools-<date>.md. Promote the ledger: full matrix with the evidence column, the issues-found-and-fixed section with commit hashes, methodology caveats, and non-blocking follow-ups.
  • Every number verified — sample count from ls samples/ (minus shared-resources) and cli/samples.json, never from memory.
  • CI green on the fix commits before the release proceeds — all workflows, not just the one you were watching.
  • Update memory with anything reusable: a new trap, a new drive recipe, a changed port, a sample added or removed.

Files in this skill

FileUse
references/sample-matrix.mdEvery sample: boot type, sweep port, drive surface, headline assertion, gating
references/driving-recipes.mdchrome-devtools call sequences per surface class + browser-layer traps
references/expo-sweep.mdStep 1b — the RN client in the iOS simulator
references/cli-sweep.mdStep 1c — what CI already covers, the real gaps, and the CLI pass
references/regression-specs.mdWhere a Playwright spec lives, how to wire it into CI, how to prove it bites
references/retest-subset.mdBlast radius → which passing samples to re-drive after a fix
references/troubleshooting.mdKnown traps: PNA, long-poll probes, stale jars, port collisions, Quarkus LLM config
assets/ledger-template.mdThe sweep ledger to copy into claude_docs/
scripts/sweep-sample.shBoot one sample from its packaged artifact on a sweep port and leave it running

© Atmosphere, 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 9 other files (scripts, references, assets) in .claude/skills/release-sample-sweep of Atmosphere/atmosphere.

  • SKILL.md
  • assets/ledger-template.md
  • references/cli-sweep.md
  • references/driving-recipes.md
  • references/expo-sweep.md
  • references/regression-specs.md
  • references/retest-subset.md
  • references/sample-matrix.md
  • references/troubleshooting.md
  • scripts/sweep-sample.sh

Open the folder on GitHubat commit 13671cf

Compare with similar skills

Release Sample Sweep 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 Sample Sweep compared with similar skills
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Release Sample Sweep this skillAtmosphere/atmosphere3.8k—~4.2kAutomated safety check: PassApache-2.0
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Appllama UsageAppllama/appllama-skills2.5k1 repos~1.6kAutomated safety check: PassMIT
Agent Cdpgronxb/codex-relay679—~956Automated safety check: PassApache-2.0
Flutter MCP Toolkit Maintain WebArenukvern/mcp_flutter386—~1.6kAutomated safety check: PassMIT
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Questions about Release Sample Sweep

What does Release Sample Sweep do?

Run the pre-release end-to-end sweep of every user-facing surface — the 33 samples under samples/ (booted from their packaged artifacts and driven in a real browser via chrome-devtools MCP), the…. Release Sample Sweep is an agent skill from Atmosphere/atmosphere. Run the pre-release end-to-end sweep of every user-facing surface — the 33 samples under samples/ (booted from their packaged artifacts and driven in a real browser via chrome-devtools MCP), the Expo/React Native client, and the atmosphere CLI.

When should I use Release Sample Sweep?

Release Sample Sweep fits situations like: tasks that involve Cross-platform mobile apps; tasks that involve Browser testing; tasks that involve LLM inference and serving.

How do I install Release Sample Sweep in Claude Code?

Run `npx skills add Atmosphere/atmosphere --skill release-sample-sweep -a claude-code`. Or copy the skill folder (.claude/skills/release-sample-sweep in Atmosphere/atmosphere) into .claude/skills/release-sample-sweep in your project. Claude Code loads it when a task matches its description.

How do I install Release Sample Sweep in Codex?

Run `npx skills add Atmosphere/atmosphere --skill release-sample-sweep -a codex`. Or copy the skill folder (.claude/skills/release-sample-sweep in Atmosphere/atmosphere) into .agents/skills/release-sample-sweep in your project. Codex loads it when a task matches its description.

Can I use Release Sample Sweep 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 Atmosphere/atmosphere --skill release-sample-sweep -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-sample-sweep, .gemini/skills/release-sample-sweep, .github/skills/release-sample-sweep and .opencode/skills/release-sample-sweep in your project.

What does Release Sample Sweep need to run?

Going by SKILL.md and its folder, Release Sample Sweep needs a shell for the scripts in its folder, the command-line tools its instructions call (git, ollama, curl, java and claude) and credentials named LLM_API_KEY. Our summary lists: A Bash shell; Docker; A credential in LLM_API_KEY.

Does Release Sample Sweep access the network?

SKILL.md contains no URLs. Its commands use git and curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Release Sample Sweep 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 Sample Sweep use?

Release Sample Sweep 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 Sample Sweep use?

About 4.2k tokens (SKILL.md is roughly 17k 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 15k tokens, read only when the agent opens those files.

What are the alternatives to Release Sample Sweep?

Skills that share tags, products or a category with Release Sample Sweep: Appllama App Design Skill (Appllama/appllama-skills, 2.5k stars), Appllama Usage (Appllama/appllama-skills, 2.5k stars), Agent Cdp (gronxb/codex-relay, 679 stars) and Flutter MCP Toolkit Maintain Web (Arenukvern/mcp_flutter, 386 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Release Sample Sweep?

Atmosphere (a GitHub organization) maintains it in Atmosphere/atmosphere, which has 3,818 GitHub stars. The repository holds 20 skills in this directory. The repository was last updated on October 6, 2026.

Source: Atmosphere/atmosphere on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.