Options
asgeirtj/system_prompts_leaks
Present multiple design options as a vertical stack of anchored turns
Prune accumulated complexity from a product — features, options, settings, code paths that no longer earn their place.
$ npx skills add hashgraph-online/awesome-codex-plugins --skill ockhams-feature-pruning -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins ockhams-feature-pruning --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "ockhams-feature-pruning" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning into .claude/skills/ockhams-feature-pruning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ockhams-feature-pruning", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruningType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add hashgraph-online/awesome-codex-plugins --skill ockhams-feature-pruning -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins ockhams-feature-pruning --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning .agents/skills/ockhams-feature-pruning && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ockhams-feature-pruning" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning into .agents/skills/ockhams-feature-pruning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ockhams-feature-pruning", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add hashgraph-online/awesome-codex-plugins --skill ockhams-feature-pruning -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins ockhams-feature-pruning --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning .cursor/skills/ockhams-feature-pruning && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ockhams-feature-pruning" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning into .cursor/skills/ockhams-feature-pruning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ockhams-feature-pruning", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/hashgraph-online/awesome-codex-plugins.git --path plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add hashgraph-online/awesome-codex-plugins --skill ockhams-feature-pruning -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins ockhams-feature-pruning --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning .gemini/skills/ockhams-feature-pruning && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ockhams-feature-pruning" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning into .gemini/skills/ockhams-feature-pruning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ockhams-feature-pruning", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install hashgraph-online/awesome-codex-plugins ockhams-feature-pruningInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add hashgraph-online/awesome-codex-plugins --skill ockhams-feature-pruning -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning .github/skills/ockhams-feature-pruning && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ockhams-feature-pruning" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning into .github/skills/ockhams-feature-pruning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ockhams-feature-pruning", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add hashgraph-online/awesome-codex-plugins --skill ockhams-feature-pruning -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins ockhams-feature-pruning --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning .opencode/skills/ockhams-feature-pruning && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ockhams-feature-pruning" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/ockhams-feature-pruning into .opencode/skills/ockhams-feature-pruning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ockhams-feature-pruning", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
ockhams-feature-pruningPrune 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. 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.
Read from SKILL.md and the folder at commit 78497e5. It shows what the files ask for, not the result of running them.
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.
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.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
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.
.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.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.
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.
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.
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:
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?
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.
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 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.
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 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 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.
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.
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.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
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.
Open the folder on GitHubat commit 78497e5
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Ockhams Feature Pruning this skillhashgraph-online/awesome-codex-plugins | 1.2k | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Optionsasgeirtj/system_prompts_leaks | 69k | — | ~918 | Automated safety check: Pass | CC0-1.0 | |
| Manage Settingsasgeirtj/system_prompts_leaks | 69k | — | ~3.1k | Automated safety check: Pass | CC0-1.0 | |
| Warp Settings Editorwarpdotdev/warp | 65k | 1 repos | ~675 | Automated safety check: Pass | AGPL-3.0 | |
| Options Strategy BacktestingHKUDS/Vibe-Trading | 35k | — | ~2k | Automated safety check: Pass | MIT | |
| Warp Settings Page Builderwarpdotdev/warp | 65k | 1 repos | ~4.5k | Automated safety check: Pass | AGPL-3.0 |
asgeirtj/system_prompts_leaks
Present multiple design options as a vertical stack of anchored turns
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…
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.
HKUDS/Vibe-Trading
Backtests multi-leg option strategies by synthesizing Black-Scholes prices from the underlying, simulating PnL, Greeks exposure and expiration for crypto and equity options.
warpdotdev/warp
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.
getsentry/sentry
Remove a Sentry option or FlagPole feature flag whose rollout is finished, in the correct PR order across sentry, getsentry, and sentry-options-automator.
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.
hashgraph-online/awesome-codex-plugins
Manage and query Calibre libraries with the calibredb CLI (local paths or Calibre Content server URLs).
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…
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…
hashgraph-online/awesome-codex-plugins
Balance game difficulty, resources, rewards, probability, progression, economies, and dominant strategies.
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…
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.
Ockhams Feature Pruning fits situations like: auditing a mature product; reducing surface area; simplifying complex workflows; evaluating whether to remove low-usage features.
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.
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.
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.
SKILL.md names no scripts, command-line tools or credentials: Ockhams Feature Pruning is instructions for the agent only.
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.
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.
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.
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.
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.
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.