Agent skill

Ockhams Feature Pruning

by hashgraph-online in hashgraph-online/awesome-codex-plugins

Prune accumulated complexity from a product — features, options, settings, code paths that no longer earn their place.

Apache-2.0Auto-check passed

Install Ockhams Feature Pruning

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill ockhams-feature-pruning -a claude-code

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

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins ockhams-feature-pruning --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/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning .claude/skills/ockhams-feature-pruning && 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
ockhams-feature-pruning
GitHub stars
1.2k
Token cost
~2.3k tokens
SKILL.md length
1,241 words
Files
2 (incl. references)
Skills in repo
686
Repo updated
First seen
Licence
Apache-2.0

At a glance

Prune accumulated complexity from a product — features, options, settings, code paths that no longer earn their place.

  • Auditing a mature product
  • SKILL.md covers What's a candidate for pruning, What's not a candidate for…, The pruning process and Pruning patterns, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Reducing surface area

What it does

Ockhams Feature Pruning is an agent skill from hashgraph-online/awesome-codex-plugins. Prune accumulated complexity from a product — features, options, settings, code paths that no longer earn their place. Use when auditing a mature product, reducing surface area, simplifying complex workflows, or evaluating whether to remove low-usage features. Pruning is harder than adding because users (often a small but vocal minority) resist removal even when usage data justifies it. The skill is identifying what to prune, deciding how, and managing the transition.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/pruning-process.md`).

The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.

When your agent uses it

  • Auditing a mature product
  • Reducing surface area
  • Simplifying complex workflows
  • Evaluating whether to remove low-usage features

Example prompts

  • “/ockhams-feature-pruning”

What it can do on your machine

Read from SKILL.md and the folder at commit 78497e5. 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

Ockhams Feature Pruning loads about 2.3k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 124 tokens; SKILL.md has 1,241 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~124
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.9k

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 hashgraph-online/awesome-codex-plugins at commit 78497e5, republished under its Apache-2.0 licence (© hashgraph-online). 1,241 words, ~2,295 tokens.

Download SKILL.mdSave it as .claude/skills/ockhams-feature-pruning/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
ockhams-feature-pruning
description
Prune accumulated complexity from a product — features, options, settings, code paths that no longer earn their place. Use when auditing a mature product, reducing surface area, simplifying complex workflows, or evaluating whether to remove low-usage features. Pruning is harder than adding because users (often a small but vocal minority) resist removal even when usage data justifies it. The skill is identifying what to prune, deciding how, and managing the transition.

Ockham's Razor — feature pruning

Feature pruning is the discipline of removing things that no longer earn their place. Most products accumulate features, options, and complexity over time; periodic pruning is necessary to prevent the product from becoming unusable through accumulated weight.

Pruning is hard. Users (often a small but loud minority) resist removal even when most users don't use the feature. Engineers may have spent significant effort building it. Marketing has highlighted it. The cost of removal feels concrete; the benefit (freedom from maintenance, simpler product, easier evolution) is diffuse.

But pruning, done well, is one of the highest-leverage product activities. A clean product evolves faster, costs less to maintain, is easier for new users to learn, and tends to keep its core users happier.

What's a candidate for pruning

A feature, option, or piece of UI is a pruning candidate when one or more of:

Low usage. Below 1% of users (often below 5%) without clear strategic justification.

High maintenance cost. Bugs, support tickets, broken integrations.

Dependency liability. Depends on third-party services, deprecated APIs, or aging infrastructure.

Confused with other features. Users don't know which to use; designers can't articulate the difference.

Originally built for a use case that's no longer relevant. The customer who requested it no longer exists; the workflow it supported has changed.

Functionality covered by other features. Redundant with newer, better-designed capabilities.

Cognitive overhead disproportionate to value. A setting that adds choice but most users don't engage with.

Holds back evolution. Removing it would unlock significant simplification or new capability.

What's not a candidate for pruning

Even if usage is low, don't prune when:

Critical for a small but high-value audience. The 1% who use the feature might be your most valuable customers.

Required for compliance, legal, or accessibility. The feature may serve a small audience but be essential for some context.

Required for a workflow that doesn't have an alternative. Even rare workflows shouldn't be eliminated unless an alternative exists.

Strategic for future direction. Some features are bets on a future use case; usage may grow.

Underutilized due to discoverability, not lack of value. Sometimes a low-usage feature is one users don't know exists; promotion may be a better answer than pruning.

The pruning process

A disciplined pruning process:

1. Audit. List candidates based on usage data, support patterns, and engineering input.

2. Investigate each candidate. For each: who uses it, why, what would they do without it? Talk to representative users if possible.

3. Categorize:

  • Prune outright (low usage, no important users dependent).
  • Prune with migration (low usage but some users dependent; provide alternatives).
  • Defer (currently rare but strategically important).
  • Keep (justified despite low usage).

4. Plan the migration for "prune with migration" candidates. What's the alternative for affected users? How will you communicate? What's the timeline?

5. Execute. Announce, deprecate, remove. Each step has its own timeline.

6. Measure. After pruning, did the predicted benefits materialize? Are affected users staying or churning?

Pruning patterns

Soft removal. Remove from default UI but keep accessible (e.g., behind a "legacy" menu). Lower cost; users who depend can still find it.

Hard removal. Remove entirely. Higher cost but cleaner result.

Replacement. Remove the old feature; provide a better alternative. Requires the alternative to actually serve the use case.

Deprecation announcement. Announce removal date in advance; let users prepare or migrate.

Sunset migration. Active outreach to users of the feature, helping them migrate.

The right pattern depends on usage volume, importance to users, and how cleanly the alternative covers the use case.

Worked examples

A feature with declining usage

A productivity tool has a "smart suggestions" feature that auto-recommends actions. Initially popular; over time, usage has declined as the product has evolved. Audit shows: 2% of users use it weekly; mostly long-term users; the suggestions are often ignored.

Decision: prune. Announce removal in 60 days. Email the 2% explaining the change and offering alternatives. Remove the feature; gain the maintenance time back.

Result: minimal churn; the product is simpler; the engineering team has more capacity for other work.

A configuration setting that no one understands

A settings panel has a checkbox: "Enable advanced mode." When enabled, it changes some keyboard shortcuts. Audit shows: 0.3% of users have enabled it; support sometimes gets questions about it; documentation is unclear.

Decision: prune. Remove the setting; pick the better default for both modes; eliminate the divergent code paths. Communicate the change to the small affected audience.

Result: simpler settings, simpler code, easier to support.

Show full SKILL.md (497 more words)Show less
A feature dependent on a deprecated API

The product integrates with a third-party service via an API the third party is deprecating. Investigation reveals: the integration is used by 1% of users; building on the new API would take months; the use case is partially served by other integrations.

Decision: deprecate the integration; remove it when the old API shuts down. Communicate to affected users; recommend alternative integrations.

Result: avoided months of engineering work; users have alternatives.

A "kept" decision

A product has a feature for converting documents to a niche format. Usage: 0.5%. But investigation reveals that the niche format is required by some government agencies; users who depend on it are mission-critical (legal, government, healthcare).

Decision: keep, despite low usage. Document the strategic reason. Plan to maintain it; budget for ongoing support.

Result: low usage but real importance; pruning would harm critical users.

A redundant pair of features

A product has two ways to accomplish the same task: an older "import" workflow and a newer "drag to upload" feature. Both exist in the UI. Users sometimes use both; sometimes use only one.

Decision: prune the older "import" workflow. Verify the newer "drag to upload" handles all the cases. Communicate to users who used the older flow. Remove the older flow once migration is complete.

Result: cleaner UI, less code, less confusion.

Anti-patterns

Pruning what users actually need. Removing a feature without verifying who depends on it. The 1% may be your best customers.

Mass deprecation without migration paths. Announcing many removals at once with no clear alternatives. Users feel abandoned.

Soft removal that lingers forever. Moving a feature to a "legacy" menu but never actually removing it. Maintenance cost continues; the simplification benefit is partial.

Pruning on a whim. Removing things because someone subjectively thinks they're not needed, without data. Often removes things that are actually used.

No communication. Pruning silently. Users discover the change when their workflow breaks. Trust is damaged.

Pruning during major rebuilds. Major rebuilds (often called "v2") that strip many features. Almost always less successful than incremental pruning.

Reflexive feature-cutting. Treating "shipped fewer features" as a virtue independent of context. Sometimes adding features is correct; sometimes pruning is. Be evidence-based.

Heuristic checklist

When considering pruning, ask: What's the actual usage of this feature? Get data before deciding. Who uses it, and why? Talk to affected users. What's the maintenance cost? Engineering time, support load, complexity. What's the alternative for users who depend? Don't leave them stranded. Is there a migration plan? Don't just delete and walk away. How will you communicate? Surprise removal damages trust.

  • ockhams-razor — parent principle on preferring simpler designs.
  • ockhams-equivalent-designs — sibling skill on identifying when designs are functionally equivalent.
  • 80-20-rule — most use is concentrated in a few features; the long tail is often pruning candidates.
  • iteration — pruning is part of the iterative process of product evolution.
  • weakest-link — sometimes the weakest link is a feature that should be removed.

See also

  • references/pruning-process.md — step-by-step process for pruning decisions.

© hashgraph-online, 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 (references) in plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • references/pruning-process.md

Open the folder on GitHubat commit 78497e5

Compare with similar skills

Ockhams Feature Pruning 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.

Ockhams Feature Pruning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ockhams Feature Pruning this skillhashgraph-online/awesome-codex-plugins1.2k—~2.3kAutomated safety check: PassApache-2.0
Optionsasgeirtj/system_prompts_leaks69k—~918Automated safety check: PassCC0-1.0
Manage Settingsasgeirtj/system_prompts_leaks69k—~3.1kAutomated safety check: PassCC0-1.0
Warp Settings Editorwarpdotdev/warp65k1 repos~675Automated safety check: PassAGPL-3.0
Options Strategy BacktestingHKUDS/Vibe-Trading35k—~2kAutomated safety check: PassMIT
Warp Settings Page Builderwarpdotdev/warp65k1 repos~4.5kAutomated safety check: PassAGPL-3.0

Similar skills

  • Options

    asgeirtj/system_prompts_leaks

    Present multiple design options as a vertical stack of anchored turns

    69k GitHub stars~918 tokensUpdated today
    Auto-check passed
  • Manage Settings

    asgeirtj/system_prompts_leaks

    Any explicit Muse Code setting question or change (model, reasoning effort, /settings) requires a silent readskill call for bundled:manage-settings as FIRST ACTION—no assistant text or other tool…

    69k GitHub stars~3.1k tokensUpdated today
    Auto-check passed
  • Warp Settings Editor

    warpdotdev/warp

    Finds and changes Warp application settings by searching a bundled JSON schema and editing the matching TOML settings file at the right nesting depth.

    65k GitHub starsUsed in 1 repo~675 tokens
    Productivity & AutomationAuto-check passed
  • Backtests multi-leg option strategies by synthesizing Black-Scholes prices from the underlying, simulating PnL, Greeks exposure and expiration for crypto and equity options.

    35k GitHub stars~2k tokensUpdated today
    Business, Finance & HRAuto-check passed
  • How to build a settings page in the Warp desktop client so widgets, page titles and settings search behave correctly, and which common mistakes to avoid.

    65k GitHub starsUsed in 1 repo~4.5k tokens
    Frontend & DesignAuto-check passed
  • Remove Option Or Flag

    getsentry/sentry

    Official

    Remove a Sentry option or FlagPole feature flag whose rollout is finished, in the correct PR order across sentry, getsentry, and sentry-options-automator.

    46k GitHub stars~3k tokensUpdated today
    Auto-check passed

More from hashgraph-online/awesome-codex-plugins

All 686 skills in this repo
  • Anime Reaction Gif

    hashgraph-online/awesome-codex-plugins

    Create original anime-style reaction stickers as looping GIFs and MP4 previews, using generated character pose sheets and timed key poses.

    1.2k GitHub stars~922 tokensUpdated today
    Auto-check passed
  • Calibredb

    hashgraph-online/awesome-codex-plugins

    Manage and query Calibre libraries with the calibredb CLI (local paths or Calibre Content server URLs).

    1.2k GitHub stars~1k tokensUpdated today
    Auto-check passed
  • Rust API Test Harness

    hashgraph-online/awesome-codex-plugins

    A skill your agent uses when adding, changing, testing, or debugging Rust HTTP APIs and services, especially when Codex needs black-box integration tests, random-port app startup, real database test…

    1.2k GitHub stars~1.7k tokensUpdated today
    Auto-check passed
  • Art

    hashgraph-online/awesome-codex-plugins

    Make a studio's game look like something at build time — a cover from a real frame of the game (free), painted covers, backdrops, textures and character plates from image models through the…

    1.2k GitHub stars~2.4k tokensUpdated today
    Auto-check passed
  • Game Balance Economy

    hashgraph-online/awesome-codex-plugins

    Balance game difficulty, resources, rewards, probability, progression, economies, and dominant strategies.

    1.2k GitHub stars~618 tokensUpdated today
    Auto-check passed
  • Manuscript Engagement Analytics

    hashgraph-online/awesome-codex-plugins

    Analyze nonfiction manuscripts for reader engagement signals, including heading-level word counts, slow starts, long slogs, weak takeaway titles, value pacing, beta-reader comment dropoff, and…

    1.2k GitHub stars~875 tokensUpdated today
    Auto-check passed

Questions about Ockhams Feature Pruning

What does Ockhams Feature Pruning do?

Prune accumulated complexity from a product — features, options, settings, code paths that no longer earn their place. Ockhams Feature Pruning is an agent skill from hashgraph-online/awesome-codex-plugins. Prune accumulated complexity from a product — features, options, settings, code paths that no longer earn their place.

When should I use Ockhams Feature Pruning?

Ockhams Feature Pruning fits situations like: auditing a mature product; reducing surface area; simplifying complex workflows; evaluating whether to remove low-usage features.

How do I install Ockhams Feature Pruning in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill ockhams-feature-pruning -a claude-code`. Or copy the skill folder (plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning in hashgraph-online/awesome-codex-plugins) into .claude/skills/ockhams-feature-pruning in your project. Claude Code loads it when a task matches its description.

How do I install Ockhams Feature Pruning in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill ockhams-feature-pruning -a codex`. Or copy the skill folder (plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning in hashgraph-online/awesome-codex-plugins) into .agents/skills/ockhams-feature-pruning in your project. Codex loads it when a task matches its description.

Can I use Ockhams Feature Pruning 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 hashgraph-online/awesome-codex-plugins --skill ockhams-feature-pruning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ockhams-feature-pruning, .gemini/skills/ockhams-feature-pruning, .github/skills/ockhams-feature-pruning and .opencode/skills/ockhams-feature-pruning in your project.

What does Ockhams Feature Pruning need to run?

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

Does Ockhams Feature Pruning 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 Ockhams Feature Pruning 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 Ockhams Feature Pruning use?

Ockhams Feature Pruning 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 Ockhams Feature Pruning use?

About 2.3k tokens (SKILL.md is roughly 9.2k 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 1.6k tokens, read only when the agent opens those files.

What are the alternatives to Ockhams Feature Pruning?

Skills that share tags, products or a category with Ockhams Feature Pruning: Options (asgeirtj/system_prompts_leaks, 69k stars), Manage Settings (asgeirtj/system_prompts_leaks, 69k stars), Warp Settings Editor (warpdotdev/warp, 65k stars) and Options Strategy Backtesting (HKUDS/Vibe-Trading, 35k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ockhams Feature Pruning?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,242 GitHub stars. The repository holds 686 skills in this directory. The repository was last updated on October 8, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.