Recognition Rewards
sickn33/agentic-awesome-skills
Recognition register: employee, reward type, category, visibility, message and points awarded.
A skill your agent uses whenever the user must locate or choose something from many possibilities — pickers, command palettes, navigation menus, autocompletes, recents lists, search results…
$ npx skills add hashgraph-online/awesome-codex-plugins --skill recognition-over-recall -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins recognition-over-recall --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/cognition-and-learnability-principles/skills/recognition-over-recall .claude/skills/recognition-over-recall && 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 "recognition-over-recall" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/recognition-over-recall into .claude/skills/recognition-over-recall/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recognition-over-recall", 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/cognition-and-learnability-principles/skills/recognition-over-recallType 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 recognition-over-recall -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins recognition-over-recall --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/cognition-and-learnability-principles/skills/recognition-over-recall .agents/skills/recognition-over-recall && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "recognition-over-recall" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/recognition-over-recall into .agents/skills/recognition-over-recall/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recognition-over-recall", 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 recognition-over-recall -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins recognition-over-recall --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/cognition-and-learnability-principles/skills/recognition-over-recall .cursor/skills/recognition-over-recall && 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 "recognition-over-recall" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/recognition-over-recall into .cursor/skills/recognition-over-recall/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recognition-over-recall", 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/cognition-and-learnability-principles/skills/recognition-over-recall--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 recognition-over-recall -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins recognition-over-recall --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/cognition-and-learnability-principles/skills/recognition-over-recall .gemini/skills/recognition-over-recall && 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 "recognition-over-recall" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/recognition-over-recall into .gemini/skills/recognition-over-recall/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recognition-over-recall", 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 recognition-over-recallInstalls 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 recognition-over-recall -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/cognition-and-learnability-principles/skills/recognition-over-recall .github/skills/recognition-over-recall && 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 "recognition-over-recall" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/recognition-over-recall into .github/skills/recognition-over-recall/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recognition-over-recall", 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 recognition-over-recall -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 recognition-over-recall --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/cognition-and-learnability-principles/skills/recognition-over-recall .opencode/skills/recognition-over-recall && 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 "recognition-over-recall" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/recognition-over-recall into .opencode/skills/recognition-over-recall/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recognition-over-recall", 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.
recognition-over-recallA skill your agent uses whenever the user must locate or choose something from many possibilities — pickers, command palettes, navigation menus, autocompletes, recents lists, search results…
Recognition Over Recall is an agent skill from hashgraph-online/awesome-codex-plugins. Use this skill whenever the user must locate or choose something from many possibilities — pickers, command palettes, navigation menus, autocompletes, recents lists, search results, settings panels. Trigger when designing inputs that ask the user to "type the right thing," when picking between dropdowns and search-first interfaces, when reviewing why users keep typing wrong values into autocomplete fields. Recognition Over Recall is one of the foundational principles in 'Universal Principles of Design' (Lidwell…
Its SKILL.md is about 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/research-and-cases.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.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 3e1456a. 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.
Shell commands in SKILL.md call:
gitFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
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.
Recognition Over Recall loads about 3k tokens when it runs, and up to ~3.6k if it reads all its reference files. Until then it costs about 167 tokens; SKILL.md has 1,347 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 3e1456a, republished under its Apache-2.0 licence (© hashgraph-online). 1,347 words, ~3,032 tokens.
.claude/skills/recognition-over-recall/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.People are dramatically better at recognizing things they've previously seen than at recalling them from memory. A multiple-choice question is easier than a fill-in-the-blank because the options are visible — the user only has to recognize the right one rather than retrieve it. The same principle holds across UI design: showing options beats asking the user to type from memory; using familiar conventions beats requiring the user to recall arbitrary commands.
Recognition memory and recall memory work differently in human cognition. Recognition is fast, relatively effortless, and survives long after the original exposure. Recall requires active retrieval — searching through memory for the right item — and is slower, more error-prone, and degrades faster over time. Designs that show options for the user to recognize from outperform designs that ask the user to recall and type. The classic case in UI design: the shift from command-line interfaces (recall: "what was that command?") to graphical menus (recognition: "I see what I want; click it") dramatically expanded who could use computers.
Working memory is small (~4 items). Long-term memory is vast but accessed primarily through cues. Recognition tasks provide the cue (the option is visible); recall tasks ask the brain to generate the cue from scratch.
For UI design:
The discipline: prefer surfaces that show what's available over surfaces that ask the user to remember.
A few cases where recall outperforms recognition:
The user types a few characters; the system filters from a known set; the user recognizes and selects.
<combobox label="Country">
<input placeholder="Search country..." />
<listbox>
<option value="us">United States</option>
<option value="uk">United Kingdom</option>
<option value="de">Germany</option>
<!-- 192 more -->
</listbox>
</combobox>Better than:
The most-recent or most-used items are surfaced first. Repeat actions become recognition tasks ("there it is again").
Recently used:
• Projects > Acme Q4
• Reports > Monthly summary
• Inbox
Other workspaces:
• Projects > ...The palette shows available commands; the user filters by typing partial recall and recognizes the rest. Combines fast keyboard access (for power users) with recognition (for occasional users).
A sidebar showing available sections is recognition-based. The user sees what's there. Compare to command-line where the user must recall the command name.
A date picker shows a calendar; the user clicks the date. Compare to a free-text "MM/DD/YYYY" field that requires the user to recall the format.
<input type="date" /> <!-- shows calendar picker; recognition -->
<!-- vs. -->
<input type="text" placeholder="MM/DD/YYYY" /> <!-- recall required --><input name="email" type="email" autocomplete="email" />The browser offers previously-used values; the user recognizes and selects.
The book makes a related point: recognition often dominates decision-making. People choose the familiar option even when an unfamiliar option is objectively better. Examples:
For product design, this means familiarity is a competitive moat. Users who know your product's interaction patterns will choose your product over alternatives requiring re-learning.
Bad (recall):
<input name="timezone" placeholder="Type your timezone" />Good (recognition):
<select name="timezone">
<option>America/Los_Angeles (PST/PDT)</option>
<option>America/New_York (EST/EDT)</option>
<option>Europe/London (GMT/BST)</option>
<!-- 200+ more -->
</select>Better (recognition + filtering):
<combobox name="timezone">
<input placeholder="Search..." />
<listbox>
<group label="Recent">
<option>America/Los_Angeles</option>
</group>
<group label="All">
<option>...</option>
</group>
</listbox>
</combobox>The combobox version: recents at top (recognition), filterable for the rest.
CLI (recall):
$ git checkout -b feature/new-thingCommand palette (recognition):
[ Cmd-K opens palette ]
Search: "create branch"
> Create branch
> Switch to branch
> Delete branchBoth reach the same outcome. The CLI is faster for users who recall; the palette is faster for users who don't. Modern dev tools offer both.
Early word processors (WordStar, vi) required users to recall command sequences. Modern word processors show menus; the user recognizes the option.
The recognition-based approach made word processors mass-market; the recall-based approach kept them tools for the technical.
Open file...
Recent files:
- resume-2026.docx
- project-notes.md
- budget.xlsx
Browse:
Documents/
Downloads/
Desktop/Recents collapse the recall task (where did I put it?) to recognition (there it is).
The classic recognition-over-recall demonstration. Multiple choice exams are easier than essay or fill-in-the-blank because they provide cues. Test designers calibrate by limiting how cue-rich the choices are.
A printed menu at a restaurant lets diners recognize options. Asking diners to "tell us what you want" without a menu would dramatically slow ordering and limit choice variety.
Stocked shelves where products are visible enable recognition-based shopping. Compare to bartender ordering ("what cocktails do you have?"), where customers must recall.
Pre-digital libraries provided card catalogs (recognition: browse cards) and shelf browsing (recognition: see books). Modern search engines combine both: type partial recall, recognize from results.
exposure-effect — exposure builds recognition; familiar items become preferred.serial-position-effects — first and last items in a list are recognized fastest.visibility — visible options enable recognition; hidden options don't.hicks-law — recognition collapses Hick's Law for typeahead-filtered options.mental-model — recognition leverages prior models; recall asks for explicit retrieval.mimicry — borrowing familiar patterns leverages recognition.recognition-pickers-and-palettes — pickers, dropdowns, comboboxes, command palettes; recognition-based selection patterns.recognition-recents-and-suggestions — using recents, frequently-used, and contextual suggestions to accelerate repeat tasks.Recognition Over Recall is one of the cheapest cognition wins in design. Showing options where the user would otherwise have to remember reduces error rates, speeds tasks, and dramatically widens the user base. The discipline is recognizing each input as a recall-vs-recognition choice and defaulting to recognition unless there's a specific reason not to.
© 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/cognition-and-learnability-principles/skills/recognition-over-recall of hashgraph-online/awesome-codex-plugins.
Open the folder on GitHubat commit 3e1456a
Recognition Over Recall 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 |
|---|---|---|---|---|---|---|
| Recognition Over Recall this skillhashgraph-online/awesome-codex-plugins | 1.3k | — | ~3k | Automated safety check: Pass | Apache-2.0 | |
| Recognition Rewardssickn33/agentic-awesome-skills | 47k | 1 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Logic Locatesickn33/agentic-awesome-skills | 47k | 1 repos | ~977 | Automated safety check: Pass | MIT | |
| Recallcursor/plugins | 11k | 7 repos | ~1.3k | Automated safety check: Pass | None | |
| Intent Recognitionn8n-io/n8n | 207k | — | ~7.1k | Automated safety check: Pass | Custom licence | |
| Agentmemory Recallrohitg00/agentmemory | 29k | — | ~557 | Automated safety check: Pass | Apache-2.0 |
sickn33/agentic-awesome-skills
Recognition register: employee, reward type, category, visibility, message and points awarded.
sickn33/agentic-awesome-skills
Locate the root cause of a CONFIRMED failure via backward-then-forward semi-formal tracing.
cursor/plugins
Reconstruct your recent working context from your own chat history, live state, and the shared record (user reports, prior fixes, incidents), then hand back a tight current-state brief.
n8n-io/n8n
Classifies automation requests using two decisions: anchor (which primitive owns the top-level control flow — workflow-anchored, agent-anchored, needs-clarification, or out-of-scope) and embedsother…
rohitg00/agentmemory
Searches agentmemory for past observations, sessions and learnings with hybrid keyword, vector and graph search, and reports only what comes back.
topoteretes/cognee
Explains how to query cognee agent memory with recall(): how the search type is chosen, how to narrow a query to datasets, and what the returned results contain.
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
Use CALL-E from Codex through the calle CLI. An agent skill from hashgraph-online/awesome-codex-plugins.
hashgraph-online/awesome-codex-plugins
Balance game difficulty, resources, rewards, probability, progression, economies, and dominant strategies.
A skill your agent uses whenever the user must locate or choose something from many possibilities — pickers, command palettes, navigation menus, autocompletes, recents lists, search results…. Recognition Over Recall is an agent skill from hashgraph-online/awesome-codex-plugins. Use this skill whenever the user must locate or choose something from many possibilities — pickers, command palettes, navigation menus, autocompletes, recents lists, search results, settings panels.
Recognition Over Recall fits situations like: the user must locate; choose something from many possibilities — pickers; command palettes; navigation menus.
Run `npx skills add hashgraph-online/awesome-codex-plugins --skill recognition-over-recall -a claude-code`. Or copy the skill folder (plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/recognition-over-recall in hashgraph-online/awesome-codex-plugins) into .claude/skills/recognition-over-recall in your project. Claude Code loads it when a task matches its description.
Run `npx skills add hashgraph-online/awesome-codex-plugins --skill recognition-over-recall -a codex`. Or copy the skill folder (plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/recognition-over-recall in hashgraph-online/awesome-codex-plugins) into .agents/skills/recognition-over-recall 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 recognition-over-recall -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/recognition-over-recall, .gemini/skills/recognition-over-recall, .github/skills/recognition-over-recall and .opencode/skills/recognition-over-recall in your project.
Going by SKILL.md and its folder, Recognition Over Recall needs the command-line tools its instructions call (git).
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. 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.
Recognition Over Recall 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 3k tokens (SKILL.md is roughly 12k 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 567 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Recognition Over Recall: Recognition Rewards (sickn33/agentic-awesome-skills, 47k stars), Logic Locate (sickn33/agentic-awesome-skills, 47k stars), Recall (cursor/plugins, 11k stars) and Intent Recognition (n8n-io/n8n, 207k 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 1,267 GitHub stars. The repository holds 716 skills in this directory. The repository was last updated on October 10, 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.