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Agent skill

Rudder Performance Maintainer

by Undertone0809 in Undertone0809/rudder

A skill your agent uses for repeatable Rudder performance audits, regressions, profiling, optimization proposals, or implementation verification across Messenger, Chat, Issues, Runs, Desktop, API…

Apache-2.0Auto-check passedSales & Support

Install Rudder Performance Maintainer

skills CLI
$ npx skills add Undertone0809/rudder --skill rudder-performance-maintainer -a claude-code

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

GitHub CLI
$ gh skill install Undertone0809/rudder rudder-performance-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/rudder-performance-maintainer .claude/skills/rudder-performance-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
rudder-performance-maintainer
GitHub stars
292
Token cost
~1.6k tokens
SKILL.md length
663 words
Files
6 (incl. references)
Skills in repo
30
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses for repeatable Rudder performance audits, regressions, profiling, optimization proposals, or implementation verification across Messenger, Chat, Issues, Runs, Desktop, API…

  • Works in 5 steps: Source identity — record repository SHA,… → Comparable workload — use the same seed… → Correctness sentinels — preserve… → …
  • Repeatable Rudder performance audits
  • SKILL.md covers Decide The Mode, Read The Relevant References, First-Principles Performance… and Required Gates, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Rudder Performance Maintainer is an agent skill from Undertone0809/rudder. Use for repeatable Rudder performance audits, regressions, profiling, optimization proposals, or implementation verification across Messenger, Chat, Issues, Runs, Desktop, API payloads, database queries, rendering, memory, and high-volume workflows. Trigger for daily performance checks, product latency or memory growth, prod-shaped scale tests, before/after benchmarks, virtualization or polling investigations, and requests to prove an optimization. Do not use for dev-startup recovery, one failed agent run…

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `evals/evals.json`, `references/environment-and-safety.md` and `references/evidence-model.md`).

It sits in Sales & Support, covering Proposals and quotes. 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

  • Repeatable Rudder performance audits
  • Optimization proposals
  • Implementation verification across Messenger
  • Database queries

Example prompts

  • “/rudder-performance-maintainer”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Source identity — record repository SHA, build identity, runtime
  2. Comparable workload — use the same seed manifest, scale, anchor,
  3. Correctness sentinels — preserve organization boundaries, ordering,
  4. Bounded evidence — record request count/bytes, query/service timing,
  5. Mutation ledger — list disposable records, runtime/process changes,

What it can do on your machine

Read from SKILL.md and the folder at commit e2ba0f1. 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.

    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

Rudder Performance Maintainer loads about 1.6k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 164 tokens; SKILL.md has 663 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~164
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4k

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 e2ba0f1, republished under its Apache-2.0 licence (© Undertone0809). 663 words, ~1,610 tokens.

Download SKILL.mdSave it as .claude/skills/rudder-performance-maintainer/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
rudder-performance-maintainer
description
Use for repeatable Rudder performance audits, regressions, profiling, optimization proposals, or implementation verification across Messenger, Chat, Issues, Runs, Desktop, API payloads, database queries, rendering, memory, and high-volume workflows. Trigger for daily performance checks, product latency or memory growth, prod-shaped scale tests, before/after benchmarks, virtualization or polling investigations, and requests to prove an optimization. Do not use for dev-startup recovery, one failed agent run, transcript/debug evidence, or release-pipeline duration unless measurement shows a product performance regression.

Rudder Performance Maintainer

Measure the real bottleneck under a controlled workload, preserve correctness, and separate evidence from intuition.

Decide The Mode

Classify the request before running expensive work:

  • AUDIT: measure current health, identify the highest-leverage risk, and propose the smallest safe next step. This is the default for daily checks.
  • DIAGNOSE: reproduce a named slowdown and isolate client, server, database, payload, runtime, or environment cost.
  • IMPLEMENT: change code only when the user asked to optimize/fix/implement.
  • VERIFY: run comparable before/after evidence for an existing change.

Do not let a scheduled audit silently turn into a multi-hour implementation. When the user continues the same task with an implementation request, preserve the audit evidence and switch modes explicitly.

Route environment startup/hang recovery to rudder-desktop-dev-recovery-maintainer, a single failed run or missing transcript to debug-run-transcript-maintainer, and release workflow delay to release-maintainer. Return here only when bounded measurement identifies a product latency, throughput, rendering, payload, or memory scaling problem.

Read The Relevant References

  • Always read references/evidence-model.md.
  • Read references/workflow-benchmark.md for API/database/high-volume workflow baselines and scripts/perf/run-isolated-workflow.ts.
  • Read references/ui-scroll-benchmark.md for Chat/Messenger/Issue/Run rendering, virtualization, streaming, and scripts/perf/compare-scroll-evals.mjs.
  • Read references/environment-and-safety.md before using installed Desktop, prod-local data, large logs, or cleanup/recovery actions.

Do not load UI guidance for a database-only regression or release guidance for a local benchmark.

First-Principles Performance Model

Performance is the work performed per user-visible outcome:

text
user action
  -> requests and payloads
  -> server/query/runtime work
  -> client state propagation
  -> render/layout/paint
  -> observable latency, responsiveness, memory, and correctness

Measure the boundary where cost first grows with data volume or update frequency. A low-latency API does not prove a responsive UI; low RSS does not prove smooth frames; a fast initial view does not prove bounded background polling.

Required Gates

  1. Source identity — record repository SHA, build identity, runtime descriptor, instance, version, and UI bundle where available. Treat a stale installed build as historical shape evidence, not current-source proof.
  2. Comparable workload — use the same seed manifest, scale, anchor, viewport, browser/build mode, warmups, iterations, and interaction.
  3. Correctness sentinels — preserve organization boundaries, ordering, pagination, deep navigation, realtime completion, final persisted content, and error states while measuring.
  4. Bounded evidence — record request count/bytes, query/service timing, mounted nodes, frame intervals, long tasks, renderer task time, and the memory metric actually observed.
  5. Mutation ledger — list disposable records, runtime/process changes, writes, cleanup, and any intentionally retained evidence.

If these gates cannot be met, return a qualified finding rather than a false comparison.

Show full SKILL.md (286 more words)Show less

Standard Workflow

  1. Rewrite the concern as a falsifiable hypothesis.
  2. Establish runtime/source identity and select an evidence target: current-source isolated, packaged candidate, installed prod-local, or named external environment.
  3. For production-shaped dev checks, reuse the persistent Rudder Performance Lab organization described in references/environment-and-safety.md when it is compatible with the current schema. Do not create another large seed by default.
  4. Start with bounded metadata and summary endpoints. Do not fetch maximum transcripts or full projections until the hypothesis requires them.
  5. Run the smallest representative workload, then a production-shaped edge case where scaling risk appears.
  6. Capture correctness and performance from the same run.
  7. Identify the first scaling boundary and propose one smallest safe change.
  8. In IMPLEMENT mode, use an isolated branch/worktree when the current checkout contains unrelated changes.
  9. Run before/after on the same harness and source identities.
  10. Require independent review and black-box acceptance for implementation.
  11. Report measured improvements, unchanged metrics, regressions, skipped evidence, proxy limitations, and remaining risk.

Evidence Language

  • Say JavaScript heap, renderer task time, response bytes, or process RSS exactly; do not rename one metric as another.
  • Separate current-source proof from stale installed/prod observations.
  • Separate service/query microbenchmarks from browser/Desktop terminal proof.
  • Treat p95 from a tiny sample as directional and report sample count.
  • Tool volume or memory growth is a lead, not a root cause.
  • A performance improvement that loses messages, events, anchors, realtime state, organization isolation, or accessibility is a failed optimization.

Output

text
RESULT: GREEN | YELLOW | RED | BLOCKED
Mode:
Source/runtime identity:
Workload:
Current measurements:
Correctness gates:
Primary bottleneck:
Smallest safe action:
Before/after:
Mutation and cleanup ledger:
Evidence limits:

Use GREEN only when the requested current source/target and representative edge case pass. Use YELLOW for a proven non-incident risk worth scheduling, RED for a reproduced material regression or unsafe scaling boundary, and BLOCKED when the required environment or comparable evidence is unavailable.

© 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 5 other files (references) in .agents/skills/maintainer/rudder-performance-maintainer of Undertone0809/rudder.

  • SKILL.md
  • evals/evals.json
  • references/environment-and-safety.md
  • references/evidence-model.md
  • references/ui-scroll-benchmark.md
  • references/workflow-benchmark.md

Open the folder on GitHubat commit e2ba0f1

Compare with similar skills

Rudder Performance 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.

Rudder Performance Maintainer compared with similar skills
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Rudder Performance Maintainer this skillUndertone0809/rudder292—~1.6kAutomated safety check: PassApache-2.0
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Audit Onboarding Proposalhoangnb24/repository-harness1.2k—~4kAutomated safety check: PassMIT
No Negative EchoLB623/no-negative-echo897—~965Automated safety check: PassMIT
GEO Service Proposal Generatorzubair-trabzada/geo-seo-claude11k—~3kAutomated safety check: NotesMIT
Architectural ProposalsFritzAndFriends/SharpSite1452 repos~1.6kAutomated safety check: PassMIT

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Categories

Questions about Rudder Performance Maintainer

What does Rudder Performance Maintainer do?

A skill your agent uses for repeatable Rudder performance audits, regressions, profiling, optimization proposals, or implementation verification across Messenger, Chat, Issues, Runs, Desktop, API…. Rudder Performance Maintainer is an agent skill from Undertone0809/rudder. Use for repeatable Rudder performance audits, regressions, profiling, optimization proposals, or implementation verification across Messenger, Chat, Issues, Runs, Desktop, API payloads, database queries, rendering, memory, and high-volume workflows.

When should I use Rudder Performance Maintainer?

Rudder Performance Maintainer fits situations like: repeatable Rudder performance audits; optimization proposals; implementation verification across Messenger; database queries.

How do I install Rudder Performance Maintainer in Claude Code?

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

How do I install Rudder Performance Maintainer in Codex?

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

Can I use Rudder Performance 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 rudder-performance-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/rudder-performance-maintainer, .gemini/skills/rudder-performance-maintainer, .github/skills/rudder-performance-maintainer and .opencode/skills/rudder-performance-maintainer in your project.

What does Rudder Performance Maintainer need to run?

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

Does Rudder Performance 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 Rudder Performance 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 Rudder Performance Maintainer use?

Rudder Performance 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 Rudder Performance Maintainer use?

About 1.6k tokens (SKILL.md is roughly 6.4k 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 2.4k tokens, read only when the agent opens those files.

What are the alternatives to Rudder Performance Maintainer?

Skills that share tags, products or a category with Rudder Performance Maintainer: Doc Coauthoring (aws-samples/sample-strands-agent-with-agentcore, 195 stars), Audit Onboarding Proposal (hoangnb24/repository-harness, 1.2k stars), No Negative Echo (LB623/no-negative-echo, 897 stars) and GEO Service Proposal Generator (zubair-trabzada/geo-seo-claude, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Rudder Performance 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 9, 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.