Agent skill

Prompt Sensei

by chengzhongwei in 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.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Prompt Sensei

skills CLI
$ npx skills add chengzhongwei/Prompt-sensei --skill prompt-sensei -a claude-code

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

GitHub CLI
$ gh skill install chengzhongwei/Prompt-sensei prompt-sensei --agent claude-code

Project 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/

Facts

Skill name
prompt-sensei
GitHub stars
110
Token cost
~3.4k tokens
SKILL.md length
1,547 words
Files
49 (incl. scripts, assets)
Skills in repo
1
Repo updated
First seen
Licence
Apache-2.0

At a glance

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.

  • Works in 6 steps: Treat observe mode as active for this… → Be silent: do not announce Prompt… → Assume the hook already checked… → …
  • AI & LLM Engineering work in your project
  • SKILL.md covers Invocation, Stages, Dimensions and Observe, plus 4 more sections
  • Calls node

What it does

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.

When your agent uses it

  • AI & LLM Engineering work in your project

Example prompts

  • “/prompt-sensei”

Workflow steps

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

  1. Treat observe mode as active for this session.
  2. Be silent: do not announce Prompt Sensei, do not explain setup, do not run settings.js, and do not run observe.js --init.
  3. Assume the hook already checked autoObserve and observe consent.
  4. Answer the user's current prompt normally, then append exactly one Sensei line.
  5. Keep auto-start quiet. Do not make visible observe.js recording calls; host hooks handle prompt hashing and scored-line persistence in the…
  6. Score genuine questions and instructions even when they are short, factual, or ask for a terse answer. Skip only mechanical inputs such as…

What it can do on your machine

Read from SKILL.md and the folder at commit ea1e381. 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

    Ships 1 file in scripts/, which the agent can run.

    Shell commands in SKILL.md call:

    • node

    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

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.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from chengzhongwei/Prompt-sensei at commit ea1e381, republished under its Apache-2.0 licence (© chengzhongwei). 1,547 words, ~3,440 tokens.

Download SKILL.mdSave it as .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.
name
prompt-sensei
description
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.
argument-hint
[observe|improve|lookback|setup|help]

Prompt Sensei

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.

Invocation

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 help

Advanced 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 update

Natural-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 Code: ~/.claude/skills/prompt-sensei
  • Codex: ~/.codex/skills/prompt-sensei

For setup, settings, hooks, and lookback details, read docs/skill-flows.md only when that mode is requested.

Stages

Classify every scored prompt first:

StageUse whenScore dimensions
ExplorationUser is still figuring out the problemGoal Clarity + Privacy/Safety
DiagnosisUser has symptoms or evidenceGoal Clarity + Context Completeness + Privacy/Safety
ExecutionUser wants implementation or changesAll seven dimensions
VerificationUser wants correctness checksGoal, Context, Input Boundaries, Output Format, Verification, Privacy/Safety
Reusable workflowUser wants a checklist, template, or processGoal, Context, Input Boundaries, Constraints, Output Format, Verification, Privacy/Safety
ActionShort follow-through directive in an established sessionGoal 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.

Dimensions

Score applicable dimensions from 1 to 5:

  • Goal Clarity: desired outcome is clear
  • Context Completeness: enough background to act
  • Input Boundaries: what to read/use/focus on is clear
  • Constraints: scope limits and tradeoffs are stated
  • Output Format: response shape is specified
  • Verification: correctness checks are requested
  • Privacy/Safety: unnecessary sensitive data and unsafe operations are avoided

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:

  • 90-100: Excellent — ready for this stage
  • 70-89: Good — minor gaps
  • 50-69: Developing — clear improvements available
  • 30-49: Early stage — normal for exploration
  • 10-29: Needs work

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:

  • Privacy/safety issues outrank prompt polish.
  • Debugging: expected/actual behavior and exact errors outrank output format.
  • Implementation/refactoring: file boundaries and scope constraints outrank output format.
  • Code review/verification: diff or file scope outranks response polish.
  • Planning/documentation: decision criteria, audience, and context outrank engineering-only details.

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.

tipKindVisible tip phrase
clarify-goalname the exact outcome you want before adding details
add-context-evidenceadd the evidence that makes the problem diagnosable
add-expected-actualadd expected behavior and actual behavior before asking for a fix
add-error-outputpaste the exact error output or failing assertion when it is safe
name-file-or-functionname the file, function, command, or diff the agent should focus on
add-scope-boundaryadd one boundary such as no new dependencies, minimal diff, or no API changes
add-output-formatask for the response shape that will make the answer easiest to review
add-verification-commandend with the command, test, or edge case that proves the work
redact-sensitive-datareplace secrets, personal data, and private URLs with labeled placeholders
add-safety-checkadd confirmation, rollback, or dry-run steps before risky operations
state-decision-criteriastate the criteria the agent should use to compare options
Show full SKILL.md (816 more words)Show less

Observe

When invoked by a trusted Claude Code SessionStart hook with observe --auto-start:

  1. Treat observe mode as active for this session.
  2. Be silent: do not announce Prompt Sensei, do not explain setup, do not run settings.js, and do not run observe.js --init.
  3. Assume the hook already checked autoObserve and observe consent.
  4. Answer the user's current prompt normally, then append exactly one Sensei line.
  5. Keep auto-start quiet. Do not make visible observe.js recording calls; host hooks handle prompt hashing and scored-line persistence in the background when installed.
  6. Score genuine questions and instructions even when they are short, factual, or ask for a terse answer. Skip only mechanical inputs such as one-word acknowledgements, numeric menu choices, slash-command-only wrappers, explicit "just reply ..." tests, and context-resume summaries.

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:

  1. Say: "Prompt Sensei will be coaching this session. After each prompt, I'll add a one-line score. Type /prompt-sensei report anytime for your private summary."
  2. Check consent with node <skill-root>/dist/scripts/settings.js.
  3. If observe consent is not granted, ask with structured input first when the active host/mode exposes it: use 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.
  4. After first-time consent, follow the host-aware auto-start guidance in docs/skill-flows.md. Install only the current host's native hooks.
  5. If observe consent is already granted, do not ask again.

For each later normal user prompt while observe mode is active:

  1. Skip only truly low-signal prompts: one-word acknowledgements ("ok", "yes", "got it"), slash-command-only wrappers, numeric menu choices ("1", "2", "3"), explicit "just reply ..." tests, and context-resume summaries. Any genuine question or instruction must be scored, even when it is short, simple, factual, or asks for a terse answer. Append only:
    > **[[Sensei: skipped grading for low-signal prompt]]()**
  2. Otherwise classify stage, score applicable dimensions, choose one canonical tipKind, and write the visible tip from that tipKind's habit phrase.
  3. Record the observation:
    bash
    node <skill-root>/dist/scripts/observe.js --stage <stage> --score <1-5-composite> --task-type <type> --flags <comma-separated-flags> --tip-kind <tipKind>
  4. End the response with exactly one line:
    > **[[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]]()**

Improve

Do not activate observation mode and do not save raw prompt text.

Output:

txt
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.

Other Modes

  • /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."

Help

Show:

txt
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 stored

Tone

  • Never say "bad prompt." Say "this is a reasonable starting point for exploration."
  • One habit at a time.
  • Acknowledge stage.
  • Celebrate progress.
  • Be specific and brief.

Task 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

Files

SKILL.md and 48 other files (scripts, assets) in the repository root of chengzhongwei/Prompt-sensei.

  • SKILL.md
  • .github/DISCUSSION_TEMPLATE/scoring-feedback.yml
  • .github/ISSUE_TEMPLATE/scoring-feedback.md
  • .gitignore
  • CHANGELOG.md
  • CLAUDE.md
  • LICENSE
  • README-zh.md
  • README.md
  • assets/prompt-sensei-banner-en.jpg
  • assets/prompt-sensei-banner-zh.jpg
  • docs/advanced-setup-zh.md
  • docs/advanced-setup.md
  • docs/faq.md
  • docs/philosophy.md
  • docs/privacy.md
  • … and 33 more

Open the folder on GitHubat commit ea1e381

Compare with similar skills

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Add Uint Supportpytorch/pytorch104k2 repos~2.3kAutomated safety check: PassCustom licence
LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs13k8 repos~3kAutomated safety check: PassMIT
Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs13k8 repos~3.3kAutomated safety check: PassMIT
1passwordtrpc-group/trpc-agent-go1.9k14 repos~656Automated safety check: PassApache-2.0

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Questions about Prompt Sensei

What does Prompt Sensei do?

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.

When should I use Prompt Sensei?

Prompt Sensei fits situations like: AI & LLM Engineering work in your project.

How do I install Prompt Sensei in Claude Code?

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.

How do I install Prompt Sensei in Codex?

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.

Can I use Prompt Sensei 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 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.

What does Prompt Sensei need to run?

Going by SKILL.md and its folder, Prompt Sensei needs the command-line tools its instructions call (node).

Does Prompt Sensei 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 Prompt Sensei 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Prompt Sensei use?

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.

How many tokens does Prompt Sensei use?

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.

What are the alternatives to Prompt Sensei?

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.

Who maintains Prompt Sensei?

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.