Agent skill

Star System

by templetongroup in templetongroup/radiant

Ask the user to rate the code/output just produced on a 1-5 star scale, then ask rating-appropriate follow-up questions, log the rating, and iterate until the work reaches 4+ stars.

MITAuto-check passedDevOps & Cloud

Install Star System

skills CLI
$ npx skills add templetongroup/radiant --skill star-system -a claude-code

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

GitHub CLI
$ gh skill install templetongroup/radiant star-system --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/templetongroup/radiant.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/star-system .claude/skills/star-system && 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
star-system
GitHub stars
113
Token cost
~2.2k tokens
SKILL.md length
1,254 words
Files
2
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Ask the user to rate the code/output just produced on a 1-5 star scale, then ask rating-appropriate follow-up questions, log the rating, and iterate until the work reaches 4+ stars.

  • Works in 7 steps: Ask for the rating → Optional sub-scores (2-3 star ratings… → Ask follow-up questions scaled to the… → …
  • The user says run the star system
  • SKILL.md covers Step 1: Ask for the rating, Step 2: Optional sub-scores…, Step 3: Ask follow-up… and Step 4: Log the rating, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Star System is an agent skill from templetongroup/radiant. Ask the user to rate the code/output just produced on a 1-5 star scale, then ask rating-appropriate follow-up questions, log the rating, and iterate until the work reaches 4+ stars. Use when the user says "run the star system", "rate this", or invokes /star-system after a deliverable is complete — AND automatically after every deployment, without being asked.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `SOURCE.md`).

It sits in DevOps & Cloud, covering Deployment. The repository describes itself as: A local coding harness for Mac. Chat with coding agents across cloud and local models, watch every tool call in a live activity feed, and drive a real terminal — in one window… The licence is MIT.

When your agent uses it

  • The user says run the star system
  • Invokes /star-system after a deliverable is complete — AND automatically after every deployment
  • Without being asked

Example prompts

  • “run the star system”
  • “rate this”
  • “/star-system”

Workflow steps

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

  1. Ask for the rating
  2. Optional sub-scores (2-3 star ratings only)
  3. Ask follow-up questions scaled to the rating
  4. Log the rating
  5. Confirm the action plan, then execute
  6. Gold Standard procedure (5 stars)
  7. Re-rating loop — and after every deployment

What it can do on your machine

Read from SKILL.md and the folder at commit 94838ca. 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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    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

Star System loads about 2.2k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 1,254 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~93
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from templetongroup/radiant at commit 94838ca, republished under its MIT licence (© templetongroup). 1,254 words, ~2,173 tokens.

Download SKILL.mdSave it as .claude/skills/star-system/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
star-system
description
Ask the user to rate the code/output just produced on a 1-5 star scale, then ask rating-appropriate follow-up questions, log the rating, and iterate until the work reaches 4+ stars. Use when the user says "run the star system", "rate this", or invokes /star-system after a deliverable is complete — AND automatically after every deployment, without being asked.

Star System — Code Rating & Feedback Loop

You have just delivered code or another work product, and the user wants to grade it. Run this process exactly.

This protocol is model-agnostic and harness-agnostic: it works for any AI model under any agent harness, IDE, or plain chat window. Where a step mentions a tool or file, treat it as an example — use your harness's equivalent, and fall back to plain chat when no equivalent exists. No step may fail just because a tool is unavailable.

Step 1: Ask for the rating

Ask the user to rate the output you just produced, presenting this scale (if your harness has a structured choice/question tool, present the five options with it; otherwise ask in plain chat):

  • ★ (1) — Unacceptable. Everything is wrong. The work must be redone from scratch.
  • ★★ (2) — Significant issues. The code, UI, or another major element needs substantial rework.
  • ★★★ (3) — Acceptable. A minimum viable product — works, but not exceptional.
  • ★★★★ (4) — Exceptional. Very good; only minor tweaks needed in specific areas.
  • ★★★★★ (5) — Gold standard. Exemplary. A model that all future output in this area should be measured against.

Wait for the user's answer. Do not guess or self-assign a rating.

Step 2: Optional sub-scores (2-3 star ratings only)

If the rating is 2 or 3 stars, offer an optional breakdown so the follow-up questions target the weak dimension. Ask the user to score (1-5) any of these that apply, or skip:

  • Code quality — structure, readability, idiom, error handling
  • UI / UX — visual design, layout, interaction feel
  • Functionality — does it actually do what was asked, correctly
  • Requirements fit — was the request understood and scoped right

If the user provides sub-scores, focus Step 3's questions on the lowest-scoring dimensions and skip questions about dimensions scored 4+.

Step 3: Ask follow-up questions scaled to the rating

The lower the rating, the more you must learn before touching the code again. Ask the questions for the given rating (one message; use a structured question tool where your harness has one, free-form chat otherwise). Adapt wording to the actual deliverable — these are topics, not scripts.

1 star — full diagnostic (6-8 questions)

The output is being discarded, so re-establish requirements from zero:

  1. What was the single biggest failure — wrong functionality, wrong approach, wrong design, or wrong understanding of the request?
  2. Was the core requirement misunderstood? Restate what you built it to do and ask the user to correct it.
  3. Is there anything in the current output worth salvaging, or is it a true clean-slate restart?
  4. What should the rebuilt version do differently at the architectural/structural level?
  5. Are there examples, references, or existing code the redo should follow?
  6. What constraints (stack, style, performance, scope) were violated or missed?
  7. What would a 3-star (acceptable) version of this look like, at minimum?
  8. Anything else that went wrong that the above didn't cover?
2 stars — deep rework interview (5-6 questions)

Major surgery, not a restart:

  1. Which areas are the significant problems — code quality, UI/UX, functionality, performance, or something else? (multi-select; skip if sub-scores already answered this)
  2. For each problem area named: what specifically is wrong, and what would "fixed" look like?
  3. Which parts are working well enough to leave alone?
  4. In what order should the problems be fixed (what's most painful)?
  5. Are the issues fixable within the current approach, or does some part need a redesign?
  6. What would move this to 4 stars?
3 stars — elevation interview (3-4 questions)

It works; find out what separates MVP from exceptional:

  1. What's the biggest gap between this and what you'd call exceptional?
  2. Which areas feel most "bare minimum" — polish, edge cases, UX, code structure? (skip if sub-scores already answered this)
  3. Are there missing features or refinements that were expected but not delivered?
  4. If only one thing could be improved, what should it be?
4 stars — tweak targeting (1-3 questions)

Close to perfect; identify the specific minor tweaks:

  1. Which specific areas need the minor tweaks?
  2. For each: what exactly should change?
  3. Optionally: what small thing kept this from being 5 stars?
5 stars — no questions

Ask nothing. Instead run the Gold Standard procedure (Step 6).

Step 4: Log the rating

Every rating round is recorded. In the project being reviewed, create or append to ratings.md at the project root (create it with the structure below if absent):

markdown
# Star System Ratings

## Gold Standards
<!-- 5-star outputs live here permanently — see Step 6 -->

## Rating Log

Append each round under Rating Log as:

markdown
### <YYYY-MM-DD> — <short deliverable name> — <stars as ★ characters> (round <N>)
- **Feedback:** <1-3 bullet summary of the user's answers>
- **Plan:** <1-2 line summary of the agreed action plan, or "n/a" for 5 stars>

Use the real current date. Round number increments each time the same deliverable is re-rated (Step 7). If the project has no writable root or the user declines the log, skip silently — never block the flow on logging.

Show full SKILL.md (479 more words)Show less

Step 5: Confirm the action plan, then execute

For ratings 1-4:

  1. Play back what you heard as a concise, prioritized action plan (redo plan for 1 star; fix list for 2-4).
  2. Ask the user to confirm or correct the plan.
  3. Once confirmed, execute it.

Step 6: Gold Standard procedure (5 stars)

  1. Acknowledge the gold-standard rating.
  2. Summarize (2-4 bullets) what made this output exemplary — the patterns, structure, and decisions worth repeating.
  3. Record it permanently under the Gold Standards section of ratings.md:
markdown
### <short deliverable name> — <YYYY-MM-DD>
- **Where:** <file paths or directory of the exemplary code>
- **Why:** <the 2-4 bullets from above>
  1. If a memory system or agent notes file is available (e.g. AGENTS.md, .cursorrules, CLAUDE.md, or your harness's persistent memory), also add a one-line pointer there so future sessions measure new work in this area against this exemplar.

Step 7: Re-rating loop — and after every deployment

After executing the action plan (Step 5), return to Step 1 and ask the user to re-rate the revised output.

Ask again after every deployment, unprompted. Any time fixed work reaches the user — shipped, released, installed on their device, pushed to the environment they will look at — that is a deployment, and it ends with the Step 1 question. Do not wait to be asked, do not batch several deployments into one rating, and do not decide on the user's behalf that a change was too small to be worth grading. The user should never have to remember to ask for the star system; the loop is what makes the rating a habit instead of an event.

Two things this is not. It is not a rating on work the user cannot see yet — if a change is committed but not deployed, finish deploying first. And it is not a prompt after every message: a turn that only investigates, answers a question, or reports findings has deployed nothing and ends normally.

  • Repeat the full cycle — rate, question, log (incrementing the round number), plan, fix — until the user gives 4 or 5 stars, or explicitly says to stop.
  • On each round, only ask about what's still wrong; never re-ask settled questions.
  • If a round's rating goes down, treat it as a signal the plan was wrong — ask what the fix broke or missed before planning again.
  • When the loop ends at 4+ stars, note the final rating in the log and stop.

Rules

  • Never skip Step 1 or assume a rating from context.
  • Every deployment ends with the Step 1 question, unprompted (Step 7).
  • Never argue with or negotiate the rating. The rating is the user's verdict; your job is to extract actionable specifics.
  • Scale strictly: 1 star = most questions, 4 stars = fewest, 5 stars = zero.
  • Don't ask questions the user already answered — if they gave the rating alongside detailed feedback, only ask about the gaps.
  • Keep the whole exchange efficient: batch questions rather than dribbling them one per message, unless an answer genuinely gates the next question.

© templetongroup, MIT. 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 1 other file in .claude/skills/star-system of templetongroup/radiant.

  • SKILL.md
  • SOURCE.md

Open the folder on GitHubat commit 94838ca

Compare with similar skills

Star System 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.

Star System compared with similar skills
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Star System this skilltempletongroup/radiant113—~2.2kAutomated safety check: PassMIT
Kubeshark Installerkubeshark/kubeshark12k—~3.6kAutomated safety check: NotesApache-2.0
GreptimeDB Dev Docker ImageGreptimeTeam/greptimedb6.7k—~4kAutomated safety check: NotesApache-2.0
Mirrord Operatormetalbear-co/mirrord5.4k1 repos~4.6kAutomated safety check: PassMIT
KubeSphere ServiceMesh Managerkubesphere/kubesphere17k—~2.4kAutomated safety check: PassCustom licence
Vercelremotion-dev/remotion63k—~1.2kAutomated safety check: PassCustom licence

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Categories

Questions about Star System

What does Star System do?

Ask the user to rate the code/output just produced on a 1-5 star scale, then ask rating-appropriate follow-up questions, log the rating, and iterate until the work reaches 4+ stars. Star System is an agent skill from templetongroup/radiant. Ask the user to rate the code/output just produced on a 1-5 star scale, then ask rating-appropriate follow-up questions, log the rating, and iterate until the work reaches 4+ stars.

When should I use Star System?

Star System fits situations like: the user says run the star system; invokes /star-system after a deliverable is complete — AND automatically after every deployment; without being asked.

How do I install Star System in Claude Code?

Run `npx skills add templetongroup/radiant --skill star-system -a claude-code`. Or copy the skill folder (.claude/skills/star-system in templetongroup/radiant) into .claude/skills/star-system in your project. Claude Code loads it when a task matches its description.

How do I install Star System in Codex?

Run `npx skills add templetongroup/radiant --skill star-system -a codex`. Or copy the skill folder (.claude/skills/star-system in templetongroup/radiant) into .agents/skills/star-system in your project. Codex loads it when a task matches its description.

Can I use Star System 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 templetongroup/radiant --skill star-system -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/star-system, .gemini/skills/star-system, .github/skills/star-system and .opencode/skills/star-system in your project.

What does Star System need to run?

SKILL.md names no scripts, command-line tools or credentials: Star System is instructions for the agent only.

Does Star System 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 Star System 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. Review the folder before installing.

What licence does Star System use?

Star System is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Star System use?

About 2.2k tokens (SKILL.md is roughly 8.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Star System?

Skills that share tags, products or a category with Star System: Kubeshark Installer (kubeshark/kubeshark, 12k stars), GreptimeDB Dev Docker Image (GreptimeTeam/greptimedb, 6.7k stars), Mirrord Operator (metalbear-co/mirrord, 5.4k stars) and KubeSphere ServiceMesh Manager (kubesphere/kubesphere, 17k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Star System?

templetongroup (a GitHub user) maintains it in templetongroup/radiant, which has 113 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 6, 2026.

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