Agent Builder
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
Stage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex.
$ npx skills add chengzhongwei/Prompt-sensei --skill prompt-sensei -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install chengzhongwei/Prompt-sensei prompt-sensei --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
Claude Code skills documentation · loads skills from .claude/skills/
Install the "prompt-sensei" agent skill from https://github.com/chengzhongwei/Prompt-sensei/tree/main into .claude/skills/prompt-sensei/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-sensei", 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.
$ npx skills add chengzhongwei/Prompt-sensei --skill prompt-sensei -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install chengzhongwei/Prompt-sensei prompt-sensei --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "prompt-sensei" agent skill from https://github.com/chengzhongwei/Prompt-sensei/tree/main into .agents/skills/prompt-sensei/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-sensei", 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 chengzhongwei/Prompt-sensei --skill prompt-sensei -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install chengzhongwei/Prompt-sensei prompt-sensei --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "prompt-sensei" agent skill from https://github.com/chengzhongwei/Prompt-sensei/tree/main into .cursor/skills/prompt-sensei/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-sensei", 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.
$ npx skills add chengzhongwei/Prompt-sensei --skill prompt-sensei -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install chengzhongwei/Prompt-sensei prompt-sensei --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "prompt-sensei" agent skill from https://github.com/chengzhongwei/Prompt-sensei/tree/main into .gemini/skills/prompt-sensei/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-sensei", 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 chengzhongwei/Prompt-sensei prompt-senseiInstalls 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 chengzhongwei/Prompt-sensei --skill prompt-sensei -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "prompt-sensei" agent skill from https://github.com/chengzhongwei/Prompt-sensei/tree/main into .github/skills/prompt-sensei/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-sensei", 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 chengzhongwei/Prompt-sensei --skill prompt-sensei -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install chengzhongwei/Prompt-sensei prompt-sensei --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "prompt-sensei" agent skill from https://github.com/chengzhongwei/Prompt-sensei/tree/main into .opencode/skills/prompt-sensei/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-sensei", 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.
prompt-senseiStage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex.
Prompt Sensei is an agent skill from chengzhongwei/Prompt-sensei. Stage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex.
Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 53 other files, including scripts and assets (for example `.github/DISCUSSION_TEMPLATE/scoring-feedback.yml`, `.github/ISSUE_TEMPLATE/scoring-feedback.md` and `CHANGELOG.md`).
It sits in AI & LLM Engineering. The repository describes itself as: Local-first prompt coach for Claude Code and Codex that improves prompts, observes prompting habits, and analyzes local history with explicit consent. The licence is Apache-2.0.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit ea1e381. 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.
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
nodeFrom 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.
Prompt Sensei loads about 3.4k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 1,547 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); the scripts in this folder are not scanned.
The full file from chengzhongwei/Prompt-sensei at commit ea1e381, republished under its Apache-2.0 licence (© chengzhongwei). 1,547 words, ~3,440 tokens.
.claude/skills/prompt-sensei/SKILL.md (or your agent's skills folder). This skill also uses 48 other files; get the full folder from GitHub.Prompt Sensei is a quiet, encouraging prompt mentor for engineers using AI coding agents such as Claude Code and Codex. Be a teacher, not a judge. Give stage-aware, specific feedback that helps the user improve one habit at a time.
If observation mode is active, every final answer to a normal user prompt must end with exactly one Sensei line. Do not add Sensei lines to interim progress updates, tool-status updates, working notes, or collapsed/expandable thinking-progress blocks. Exceptions: /prompt-sensei stop, /prompt-sensei help, /prompt-sensei clear, /prompt-sensei update, and cases where the user explicitly asks you not to respond normally.
Essential commands:
/prompt-sensei or /prompt-sensei observe — activate coaching for this session/prompt-sensei observe --auto-start — silently activate coaching from trusted host hooks/prompt-sensei improve <prompt> — score and minimally improve one prompt/prompt-sensei lookback — analyze selected local Claude Code or Codex history after separate consent/prompt-sensei setup — guided setup for consent, auto-start scope, and optional redacted previews/prompt-sensei help — show concise helpAdvanced commands:
/prompt-sensei stop/prompt-sensei report/prompt-sensei settings/prompt-sensei settings auto-observe on|off|folder|user/prompt-sensei settings save-redacted-prompts on|off/prompt-sensei settings auto-observe=off save-redacted-prompts=on/prompt-sensei clear/prompt-sensei updateNatural-language equivalents in Codex include "use prompt-sensei", "improve this prompt", "look back at my prompt history", "show my prompt-sensei report", "use prompt-sensei setup", and "turn auto observe on". Settings commands accept friendly values such as enable/disable, true/false, and aliases such as redacted, previews, and auto-start.
When running scripts, use the installed skill root:
~/.claude/skills/prompt-sensei~/.codex/skills/prompt-senseiFor setup, settings, hooks, and lookback details, read docs/skill-flows.md only when that mode is requested.
Classify every scored prompt first:
| Stage | Use when | Score dimensions |
|---|---|---|
| Exploration | User is still figuring out the problem | Goal Clarity + Privacy/Safety |
| Diagnosis | User has symptoms or evidence | Goal Clarity + Context Completeness + Privacy/Safety |
| Execution | User wants implementation or changes | All seven dimensions |
| Verification | User wants correctness checks | Goal, Context, Input Boundaries, Output Format, Verification, Privacy/Safety |
| Reusable workflow | User wants a checklist, template, or process | Goal, Context, Input Boundaries, Constraints, Output Format, Verification, Privacy/Safety |
| Action | Short follow-through directive in an established session | Goal Clarity + Privacy/Safety |
Do not penalize Exploration or Action prompts for missing execution details. Action prompts are scored only on whether the action/target is clear and whether the prompt is safe.
For calibration details, use docs/scoring-rubric.md when needed.
Score applicable dimensions from 1 to 5:
Composite score: average applicable dimensions, multiplied by 20 and rounded. Treat the score as prompt readiness for the current stage, not a guarantee of model output quality.
Grade labels:
In observe mode, grade labels are not a substitute for coaching. For any scored prompt below 90, the Sensei line must include Tip: with one concrete next habit. It is okay to use a grade label like Good — minor gaps only when a concrete tip is also included.
Choose the most useful next habit, not mechanically the lowest dimension. Apply this priority:
For below-90 feedback, pick exactly one canonical tipKind before writing the visible tip. Use the matching habit phrase below, or a very close paraphrase that keeps the same keywords. Free-form tips may not persist tipKind in hook-recorded events.
| tipKind | Visible tip phrase |
|---|---|
clarify-goal | name the exact outcome you want before adding details |
add-context-evidence | add the evidence that makes the problem diagnosable |
add-expected-actual | add expected behavior and actual behavior before asking for a fix |
add-error-output | paste the exact error output or failing assertion when it is safe |
name-file-or-function | name the file, function, command, or diff the agent should focus on |
add-scope-boundary | add one boundary such as no new dependencies, minimal diff, or no API changes |
add-output-format | ask for the response shape that will make the answer easiest to review |
add-verification-command | end with the command, test, or edge case that proves the work |
redact-sensitive-data | replace secrets, personal data, and private URLs with labeled placeholders |
add-safety-check | add confirmation, rollback, or dry-run steps before risky operations |
state-decision-criteria | state the criteria the agent should use to compare options |
When invoked by a trusted Claude Code SessionStart hook with observe --auto-start:
settings.js, and do not run observe.js --init.autoObserve and observe consent.observe.js recording calls; host hooks handle prompt hashing and scored-line persistence in the background when installed.In Codex auto observe, use instruction-based observe only as a fallback for hook trust gaps. If a trusted SessionStart hook loads observe context, add exactly one Sensei line to final answers yourself so the user still gets coaching when the Stop hook is not trusted or not running. If the trusted Stop hook sees that final line, it persists it and does not request a continuation; if the final answer omitted the line, it asks for one continuation whose only content is the final Sensei line. Do not add Sensei lines to Codex progress/status updates while work is still ongoing, and never add more than one Sensei line to a final answer.
When /prompt-sensei observe starts:
/prompt-sensei report anytime for your private summary."node <skill-root>/dist/scripts/settings.js.AskUserQuestion in Claude Code or request_user_input in Codex. Keep the numbered-list yes/no prompt from docs/skill-flows.md as the fallback when structured input is unavailable. If the user grants consent, run node <skill-root>/dist/scripts/observe.js --init.For each later normal user prompt while observe mode is active:
> **[[Sensei: skipped grading for low-signal prompt]]()**tipKind, and write the visible tip from that tipKind's habit phrase.node <skill-root>/dist/scripts/observe.js --stage <stage> --score <1-5-composite> --task-type <type> --flags <comma-separated-flags> --tip-kind <tipKind>> **[[Sensei: 68/100 · Diagnosis; Tip: add the error message and file path]]()**For scores below 90, the final line must include Tip:. Never replace the tip with only a generic label such as Good — minor gaps.
Valid flags: missing-context, no-constraints, no-verification, no-output-format, missing-input-boundaries, privacy-risk, safety-risk. Omit --flags for Action prompts unless there is a real privacy/safety issue.
Only for scores 90+, use encouragement instead of a tip:
> **[[Sensei: 94/100 · Execution; Excellent — ready for this stage]]()**Do not activate observation mode and do not save raw prompt text.
Output:
Prompt Sensei Improve
=====================
Stage: Execution
Score: 68 / 100 (Developing)
What is missing:
- error output
- file path
- verification command
Improved prompt:
[copyable prompt here]
Habit to practice next:
Add expected behavior and actual behavior before asking for a fix.Preserve the user's intent. Add only the highest-impact missing details or placeholders. Always end with exactly one habit to practice next.
/prompt-sensei setup: read docs/skill-flows.md, then use one combined structured-input call on either host when the active mode exposes it: AskUserQuestion in Claude Code or request_user_input in Codex, with up to three questions for consent, auto-start, and redacted previews. Skip questions whose answers are already known. If structured input is unavailable, briefly explain the mode limitation and use the numbered fallback. On Codex, install Codex hooks for auto-start when requested and tell the user that a trust prompt for new or changed command hooks is expected; they should inspect the command paths with /hooks before enabling them./prompt-sensei settings: if the user asks to show settings, run node <skill-root>/dist/scripts/settings.js and show compact output. If the user asks to configure settings without exact values, read docs/skill-flows.md and use the Settings Mode structured picker when available; fall back to a numbered list when structured input is unavailable. If the user provides exact values, run the matching settings.js command directly./prompt-sensei lookback: read docs/skill-flows.md, then follow the scoped-consent lookback flow./prompt-sensei report: run node <skill-root>/dist/scripts/report.js and display its Markdown output./prompt-sensei clear: run node <skill-root>/dist/scripts/clear.js./prompt-sensei update: run node <skill-root>/dist/scripts/update.js --apply./prompt-sensei stop: stop scoring and say "Prompt Sensei has stopped observing. Type /prompt-sensei observe to resume."Show:
Prompt Sensei — a quiet prompt mentor for AI coding agents
Commands:
/prompt-sensei observe Score prompts as you write them
/prompt-sensei improve "<prompt>" Rewrite a prompt with one teaching note
/prompt-sensei lookback Analyze selected local prompt history
/prompt-sensei setup Configure auto-start and privacy options
/prompt-sensei help Show this help
More:
/prompt-sensei stop Stop scoring for this session
/prompt-sensei report Show your private summary
/prompt-sensei settings Show local settings
/prompt-sensei update Pull the latest version and rebuild
/prompt-sensei clear Delete local Prompt Sensei data
Storage: ~/.prompt-sensei/ — local only, no cloud
Privacy: Raw prompt text is never storedTask types: debugging, implementation, code-review, refactoring, architecture, planning, documentation, testing, exploration, other.
© chengzhongwei, 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 48 other files (scripts, assets) in the repository root of chengzhongwei/Prompt-sensei.
Open the folder on GitHubat commit ea1e381
Prompt Sensei 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 |
|---|---|---|---|---|---|---|
| Prompt Sensei this skillchengzhongwei/Prompt-sensei | 110 | — | ~3.4k | Automated safety check: Pass | Apache-2.0 | |
| Agent BuildershareAI-lab/learn-claude-code | 78k | 4 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3.3k | Automated safety check: Pass | MIT | |
| 1passwordtrpc-group/trpc-agent-go | 1.9k | 14 repos | ~656 | Automated safety check: Pass | Apache-2.0 |
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
pytorch/pytorch
Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.
Orchestra-Research/AI-Research-SKILLs
Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.
Orchestra-Research/AI-Research-SKILLs
Guide to using Meta's Segment Anything Model for zero-shot image segmentation with point, box or mask prompts, or automatic mask generation.
trpc-group/trpc-agent-go
Set up and use 1Password CLI (op). An agent skill from trpc-group/trpc-agent-go.
jarrodwatts/claude-code-config
Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage.
Categories
Stage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex. Prompt Sensei is an agent skill from chengzhongwei/Prompt-sensei. Stage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex.
Prompt Sensei fits situations like: AI & LLM Engineering work in your project.
Run `npx skills add chengzhongwei/Prompt-sensei --skill prompt-sensei -a claude-code`. Or copy the skill folder (the chengzhongwei/Prompt-sensei repository) into .claude/skills/prompt-sensei in your project. Claude Code loads it when a task matches its description.
Run `npx skills add chengzhongwei/Prompt-sensei --skill prompt-sensei -a codex`. Or copy the skill folder (the chengzhongwei/Prompt-sensei repository) into .agents/skills/prompt-sensei 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 chengzhongwei/Prompt-sensei --skill prompt-sensei -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-sensei, .gemini/skills/prompt-sensei, .github/skills/prompt-sensei and .opencode/skills/prompt-sensei in your project.
Going by SKILL.md and its folder, Prompt Sensei needs the command-line tools its instructions call (node).
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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Prompt Sensei is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Prompt Sensei: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
chengzhongwei (a GitHub user) maintains it in chengzhongwei/Prompt-sensei, which has 110 GitHub stars. The repository was last updated on July 13, 2026.
Source: chengzhongwei/Prompt-sensei on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.