Agent skill

Agent Work Reviewer Maintainer

by Undertone0809 in Undertone0809/rudder

Independently review Rudder changes or proposals for intent, correctness, product quality, and evidence.

Apache-2.0Auto-check passedAgent Workflows

Install Agent Work Reviewer Maintainer

skills CLI
$ npx skills add Undertone0809/rudder --skill agent-work-reviewer-maintainer -a claude-code

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

GitHub CLI
$ gh skill install Undertone0809/rudder agent-work-reviewer-maintainer --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/Undertone0809/rudder.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/maintainer/agent-work-reviewer-maintainer .claude/skills/agent-work-reviewer-maintainer && 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
agent-work-reviewer-maintainer
GitHub stars
292
Token cost
~3.6k tokens
SKILL.md length
1,777 words
Files
2
Skills in repo
30
Repo updated
First seen
Licence
Apache-2.0

At a glance

Independently review Rudder changes or proposals for intent, correctness, product quality, and evidence.

  • Works in 5 steps: First-Principles Intent → Functional Trust → Adversarial Risk → …
  • Tasks that involve Proposals and quotes
  • SKILL.md covers Proportional Scope, Role Boundary, Verdicts and Evidence Baseline, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Agent Work Reviewer Maintainer is an agent skill from Undertone0809/rudder. Independently review Rudder changes or proposals for intent, correctness, product quality, and evidence. Use review depth from AGENTS.md; return accept, needs more evidence, or reject with actionable findings. Does not implement fixes or replace required black-box acceptance.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `evals/evals.json`).

It sits in Agent Workflows, covering Proposals and quotes and Agent instruction files. The repository describes itself as: Open-source local Agent harness for self-improving agent teams: run agents, review work, and turn feedback into reusable skills. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Proposals and quotes
  • Tasks that involve Agent instruction files

Example prompts

  • “/agent-work-reviewer-maintainer”

Workflow steps

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

  1. First-Principles Intent
  2. Functional Trust
  3. Adversarial Risk
  4. Product Taste
  5. Evidence Integrity

What it can do on your machine

Read from SKILL.md and the folder at commit 2676a5c. 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

Agent Work Reviewer Maintainer loads about 3.6k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 1,777 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k

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 Undertone0809/rudder at commit 2676a5c, republished under its Apache-2.0 licence (© Undertone0809). 1,777 words, ~3,628 tokens.

Download SKILL.mdSave it as .claude/skills/agent-work-reviewer-maintainer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
agent-work-reviewer-maintainer
description
Independently review Rudder changes or proposals for intent, correctness, product quality, and evidence. Use review depth from AGENTS.md; return accept, needs more evidence, or reject with actionable findings. Does not implement fixes or replace required black-box acceptance.

Agent Work Reviewer Maintainer

Judge whether the work solved the right problem, produced a coherent Rudder experience, and earned the claimed level of acceptance. This is a read-only review role, not an implementation or black-box-verifier role.

Proportional Scope

Follow AGENTS.md section 9.1. Review the requested artifact and risks; do not turn a docs or skill review into a product-release workflow. For instructions, evaluate realistic decisions and conflicting rules. Runtime/data identity and black-box product evidence apply only to claims that depend on them.

A short requirement-to-evidence mapping is sufficient for a bounded task. Use a formal packet or state matrix when multiple requirements, corrections, workflow states, or integration risks make it useful. Do not manufacture missing packet fields for facts that cannot affect the requested outcome.

Role Boundary

  • Inspect the request, diff, relevant code, tests, screenshots, and running surface when they materially affect judgment.
  • Do not edit files, stage, commit, push, or fix findings during the review.
  • Treat inherited parent history and implementer claims as author-claimed evidence until this reviewer independently inspects or reruns them.
  • Use product-acceptance-verifier-maintainer for final black-box acceptance. Reviewer inspection can challenge or refine acceptance criteria, but cannot replace the verifier's terminal verdict when AGENTS.md requires that gate.

Verdicts

Return exactly one reviewer verdict and name its level:

  • accept: no blocking product, implementation, evidence, or handoff gap remains for this level.
  • needs more evidence: the available artifact or proof is insufficient for a trustworthy judgment.
  • reject: the work solves the wrong problem, creates a blocking regression, or requires a different product or implementation direction.

Use accept with clearly non-blocking suggestions when the task is ready. Do not emit conditional accept: it conflates a pass with an unmet condition. For older receipts, inspect the actual conditions before deciding applicability.

Use stage verdict for a proposal, design, or implementation slice. Use final handoff verdict only for the exact candidate that is ready to commit, merge, or deliver. A stage accept is not a final accept.

Evidence Baseline

Lock the review target before judging it:

  • request, later corrections, non-goals, and acceptance criteria
  • branch and commit SHA; whether the worktree is dirty
  • changed-file or artifact scope
  • screenshot, preview, build, runtime, organization, and data identity when relevant
  • previous verdict, blockers, and changed evidence for repeat rounds
  • author-claimed evidence versus reviewer-verified evidence
  • verifier verdict and candidate fingerprint when final acceptance is requested

Review the named artifact, not the whole shared dirty worktree. Unrelated dirty files matter only when they contaminate the diff, candidate, build, or handoff. If relevant content changes, review the changed risks again. Unrelated work or commit metadata alone does not invalidate observations of unchanged content; record the equivalence when rebinding the receipt.

Intent And Packet Alignment

Make the user's source request and later corrections machine-visible before judging implementation quality. Do not let an implementer summary replace the request baseline. For complex or corrected requests, use a compact ledger:

Raw sourceExact phrase or correctionObservable acceptance criterionPacket field/evidenceStatus
User request or later correctionQuote or link the original wordingWhat a user could observe or compareWhere the packet proves italigned / missing / mismatch

Translate spatial and relational language such as inside, at the bottom, replace, same as, next to, restore, or disappears into explicit placement, adjacency, visibility, lifecycle, or comparison criteria. Preserve later corrections as superseding constraints only when the user actually made them; do not silently narrow them into a convenient paraphrase.

An acceptance packet is aligned only when every material raw requirement and correction has a matching observable criterion and evidence plan. A missing or contradictory criterion is a blocking packet mismatch finding even when the implementation, tests, or an earlier verifier receipt satisfy the narrower packet. Do not recommend verifier execution or final acceptance until the packet is corrected; this is a review gate, not a replacement for the verifier's terminal judgment.

Review Method

1. First-Principles Intent

Ask why the task exists before asking whether the patch is clean:

  • What operator or agent job should become easier or more trustworthy?
  • Is the request a symptom of a deeper workflow or object-model problem?
  • Does this advance Rudder's real end-to-end agent-work loop?
  • Is the implementation using the right product object and interaction model?
  • Would a smaller or more durable direction solve the problem better?

A literal implementation can be technically correct and still be product-wrong.

2. Functional Trust

Trace the actor, trigger, system effect, persistence, and terminal surface. Inspect the implementation, tests, and cross-layer behavior. Check organization scope, permissions, old flows, error handling, async transitions, and the highest-risk downstream consumer. Passing typecheck or unit tests does not prove the user-visible workflow.

3. Adversarial Risk

Actively look for what the implementer was least likely to test:

  • hidden assumptions and stale closures or snapshots
  • candidate, branch, build, runtime, organization, or data mismatch
  • empty, long, loading, error, retry, refresh, reopen, and restart states
  • races, partial failure, duplicate actions, and recovery paths
  • regression of a nearby shipped capability
  • tests that prove a helper while missing the public workflow
  • unrelated files or generated artifacts mixed into a narrow change

Findings should expose a real acceptance risk, not manufacture novelty.

Show full SKILL.md (930 more words)Show less
4. Product Taste

For visible UI, read doc/engineering/DESIGN.md and inspect the rendered result. Judge the product as an operational tool, not as isolated CSS:

  • surface ratio and information hierarchy
  • density with clarity and scan speed
  • typography hierarchy and control weight
  • whitespace distribution and layout rhythm
  • progressive disclosure and copy restraint
  • cognitive load and decision sequencing
  • interaction feedback, continuity, icons, and keyboard behavior
  • consistency with the nearest shipped Rudder surface

When visual consistency or a named reference is part of the claim, inspect a comparative frame or equivalent evidence with the shipped sibling/reference. An isolated crop cannot establish same as or matching. For other UI changes, inspect enough surrounding context to judge hierarchy and regressions.

When labels, cards, or status treatments change, trace the user-facing language through open, submitting, completed, failed, cancelled/superseded, refresh, and reopen states as applicable. Terminal cards must not retain action-needed copy or expose internal attempt counters as the operator outcome. For a long or virtualized list, require evidence that load-more/reveal preserves the scroll anchor, focus, hidden-item discoverability, and stable filter/sort state across refresh or polling; deep links or search must not silently target an unmounted row. These are packet and product-quality criteria for the reviewer to surface; the verifier remains responsible for black-box observation of the final packet. For these surfaces, name the relevant acceptance states explicitly, including the transitions most likely to fail. Do not enumerate unrelated states only to mark them not applicable.

Apply a decision-load gate before visual polish:

  • Write the user's immediate job and the common-path decisions in order.
  • Check that each UI state presents one primary decision and one focal action region. A routing state may use a coherent peer choice set without promoting one route; a follow-up submit or continuation state should have one primary action.
  • Distinguish useful information density from simultaneous decision density. A compact comparison surface may show many relevant facts; a creation or configuration flow should not expose controls for future branches early.
  • Count visible choices, controls, persistent explanation, competing emphasis, and active overlays as attention cost.
  • Confirm later-step controls appear only after they become relevant, while risk, consequences, permissions, and current state remain visible when needed.
  • In every cognitive-load review, explicitly name the risk, consequence, permission, or current-state context that must remain visible. If none is identifiable from the packet, say so and name the evidence needed instead of silently treating critical context as absent.
  • Treat Reopen separately from in-flow Back, Cancel, and Close. Restoring work after Reopen is valid only when the acceptance packet defines an intentional draft contract; never infer persisted restoration from safe in-flow Back behavior or from unspecified dismissal semantics.
  • Reject a kitchen-sink first surface even when every individual control is usable and visually polished. The convergence direction should sequence the decisions and remove nonessential elements, not merely restyle them.
  • Require the proposed acceptance packet to include a compact state inventory: current decision, visible choices and controls, deferred controls, safety-critical context, primary affordance or peer choice set, and declared Back, Cancel, Close, Reopen, and draft-restoration semantics.

Use production-shaped content, not placeholder-only fixtures. Check a relevant state matrix rather than one polished screenshot:

AxisTypical states
Contentempty, normal, long/overflow, dense
Asyncloading, success, error/retry
Interactiondefault, hover, focus, keyboard, open/close
Decision flowentry, route choice, focused follow-up, back/cancel
Continuityrefresh, reopen, resize, persisted state
Viewportdesktop and constrained/mobile when supported
Themelight/dark when tokens, shell, or contrast changed

Do not require every cell mechanically. Select the states that can disprove the claim, explain omissions, and require current screenshots or live inspection for layout-sensitive final acceptance. A UI that functions but has weak hierarchy, inflated surfaces, poor density, inconsistent controls, or missing interaction states has a product-quality finding, not a nitpick.

5. Evidence Integrity

Map each claim to actual evidence:

  • code and tests establish implementation confidence
  • screenshots and browser/Desktop inspection establish rendered-state evidence
  • black-box verifier evidence establishes terminal acceptance
  • commit, CI, and release evidence establish handoff or publication state

When independent product acceptance is required, verify that product-acceptance-verifier-maintainer returned PASS for the same candidate fingerprint, runtime, organization/data identity, and acceptance packet. FAIL, QUESTION, missing proof, or candidate drift blocks final accept for that claim. Reviewer approval never upgrades a missing required verifier pass. Artifact-only review can finish without a product verifier.

Findings And Convergence

Lead with actionable findings, ordered by severity:

  • P0: unsafe, destructive, security-critical, or release-blocking
  • P1: wrong behavior, major regression, broken workflow, or untrustworthy proof
  • P2: meaningful product-quality, maintainability, or edge-case gap

For every blocker, state the evidence, user impact, and smallest credible change or proof needed. End with one convergence direction: the shortest coherent path from the current artifact to acceptance. Avoid a broad wishlist.

Output Contract

markdown
Verdict: accept | needs more evidence | reject
Level: stage verdict | final handoff verdict

Candidate and evidence baseline:
- ...

Findings:
1. [P1] ...

First-principles judgment:
- User job: ...
- Product direction: ...

UI/product-quality judgment:
- Decision sequence and focal action or peer choice set: ...
- Deferred controls: ...
- Safety-critical context retained: ...
- Back, Cancel, Close, Reopen, and draft semantics, including whether an
  intentional draft contract exists: ...
- Other product-quality evidence: ...

Evidence integrity:
- Author-claimed: ...
- Reviewer-verified: ...
- Raw intent/correction ledger: ...
- Acceptance packet alignment: aligned | mismatch | missing; blocking mismatch: ...
- Comparative UI frame and nearest sibling/reference: ...
- Terminal-state language and virtualization continuity evidence: ...
- Verifier lease: current / stale / missing / not required at this stage

Convergence direction:
- ...

Blocking conditions:
- ...

When there are no findings, say so explicitly and name residual test or visual risk. Use line-anchored code comments only for concrete source findings and keep their ranges tight.

Final Gate

A final handoff verdict can be accept only when all are true:

  1. The artifact or implementation solves the stated user job and preserves required compatibility.
  2. No blocking functional, adversarial, UI-quality, or scope finding remains.
  3. Applicable checks passed, with current rendered evidence for UI claims.
  4. When AGENTS.md section 9.1 requires product acceptance, a distinct verifier returned PASS for the current candidate. Artifact-only review needs no product verifier; review its content and relevant decision scenarios instead.
  5. Relevant evidence still applies to the current candidate.

After a fix, recheck the affected evidence. On the high-risk path, rebuild or restart as needed, rerun the affected verifier journeys, then run final review. Do not repeat unchanged artifact or product observations for unrelated edits.

© Undertone0809, 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 1 other file in .agents/skills/maintainer/agent-work-reviewer-maintainer of Undertone0809/rudder.

  • SKILL.md
  • evals/evals.json

Open the folder on GitHubat commit 2676a5c

Compare with similar skills

Agent Work Reviewer Maintainer 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.

Agent Work Reviewer Maintainer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agent Work Reviewer Maintainer this skillUndertone0809/rudder292—~3.6kAutomated safety check: PassApache-2.0
Setup Claude Mdambient-code/agentready153—~236Automated safety check: PassMIT
Self Evolveavibebuilder/claude-prime120—~1.9kAutomated safety check: PassMIT
Retro MetaNecmttn/ax115—~1.8kAutomated safety check: PassAGPL-3.0
Scoutandrew-yangy/gru-ai155—~5.6kAutomated safety check: PassMIT
Self Improvereal-simple-labs/parker-brain102—~1.4kAutomated safety check: PassCustom licence

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Questions about Agent Work Reviewer Maintainer

What does Agent Work Reviewer Maintainer do?

Independently review Rudder changes or proposals for intent, correctness, product quality, and evidence. Agent Work Reviewer Maintainer is an agent skill from Undertone0809/rudder. Independently review Rudder changes or proposals for intent, correctness, product quality, and evidence.

When should I use Agent Work Reviewer Maintainer?

Agent Work Reviewer Maintainer fits situations like: tasks that involve Proposals and quotes; tasks that involve Agent instruction files.

How do I install Agent Work Reviewer Maintainer in Claude Code?

Run `npx skills add Undertone0809/rudder --skill agent-work-reviewer-maintainer -a claude-code`. Or copy the skill folder (.agents/skills/maintainer/agent-work-reviewer-maintainer in Undertone0809/rudder) into .claude/skills/agent-work-reviewer-maintainer in your project. Claude Code loads it when a task matches its description.

How do I install Agent Work Reviewer Maintainer in Codex?

Run `npx skills add Undertone0809/rudder --skill agent-work-reviewer-maintainer -a codex`. Or copy the skill folder (.agents/skills/maintainer/agent-work-reviewer-maintainer in Undertone0809/rudder) into .agents/skills/agent-work-reviewer-maintainer in your project. Codex loads it when a task matches its description.

Can I use Agent Work Reviewer Maintainer 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 Undertone0809/rudder --skill agent-work-reviewer-maintainer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-work-reviewer-maintainer, .gemini/skills/agent-work-reviewer-maintainer, .github/skills/agent-work-reviewer-maintainer and .opencode/skills/agent-work-reviewer-maintainer in your project.

What does Agent Work Reviewer Maintainer need to run?

SKILL.md names no scripts, command-line tools or credentials: Agent Work Reviewer Maintainer is instructions for the agent only.

Does Agent Work Reviewer Maintainer 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 Agent Work Reviewer Maintainer 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 Agent Work Reviewer Maintainer use?

Agent Work Reviewer Maintainer 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 Agent Work Reviewer Maintainer use?

About 3.6k tokens (SKILL.md is roughly 15k 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 Agent Work Reviewer Maintainer?

Skills that share tags, products or a category with Agent Work Reviewer Maintainer: Setup Claude Md (ambient-code/agentready, 153 stars), Self Evolve (avibebuilder/claude-prime, 120 stars), Retro Meta (Necmttn/ax, 115 stars) and Scout (andrew-yangy/gru-ai, 155 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Work Reviewer Maintainer?

Undertone0809 (a GitHub user) maintains it in Undertone0809/rudder, which has 292 GitHub stars. The repository holds 30 skills in this directory. The repository was last updated on October 10, 2026.

Source: Undertone0809/rudder on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.