DeepTutor CLI
HKUDS/DeepTutor
Teaches the agent to set up and run DeepTutor from the command line: chat and capabilities, knowledge bases, partners, memory, sessions, notebooks and the server or Web app.
Deterministic self-evaluation rubric for Contextualizer — scored every run using the TRACE framework.
$ npx skills add ntorga/agent-starter-kit --skill contextualizer-self-review -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ntorga/agent-starter-kit contextualizer-self-review --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/ntorga/agent-starter-kit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/contextualizer-self-review .claude/skills/contextualizer-self-review && 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 "contextualizer-self-review" agent skill from https://github.com/ntorga/agent-starter-kit/tree/main/skills/contextualizer-self-review into .claude/skills/contextualizer-self-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "contextualizer-self-review", 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/ntorga/agent-starter-kit/tree/main/skills/contextualizer-self-reviewType 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 ntorga/agent-starter-kit --skill contextualizer-self-review -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ntorga/agent-starter-kit contextualizer-self-review --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ntorga/agent-starter-kit.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/contextualizer-self-review .agents/skills/contextualizer-self-review && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "contextualizer-self-review" agent skill from https://github.com/ntorga/agent-starter-kit/tree/main/skills/contextualizer-self-review into .agents/skills/contextualizer-self-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "contextualizer-self-review", 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 ntorga/agent-starter-kit --skill contextualizer-self-review -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ntorga/agent-starter-kit contextualizer-self-review --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ntorga/agent-starter-kit.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/contextualizer-self-review .cursor/skills/contextualizer-self-review && 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 "contextualizer-self-review" agent skill from https://github.com/ntorga/agent-starter-kit/tree/main/skills/contextualizer-self-review into .cursor/skills/contextualizer-self-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "contextualizer-self-review", 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/ntorga/agent-starter-kit.git --path skills/contextualizer-self-review--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 ntorga/agent-starter-kit --skill contextualizer-self-review -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ntorga/agent-starter-kit contextualizer-self-review --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ntorga/agent-starter-kit.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/contextualizer-self-review .gemini/skills/contextualizer-self-review && 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 "contextualizer-self-review" agent skill from https://github.com/ntorga/agent-starter-kit/tree/main/skills/contextualizer-self-review into .gemini/skills/contextualizer-self-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "contextualizer-self-review", 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 ntorga/agent-starter-kit contextualizer-self-reviewInstalls 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 ntorga/agent-starter-kit --skill contextualizer-self-review -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ntorga/agent-starter-kit.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/contextualizer-self-review .github/skills/contextualizer-self-review && 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 "contextualizer-self-review" agent skill from https://github.com/ntorga/agent-starter-kit/tree/main/skills/contextualizer-self-review into .github/skills/contextualizer-self-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "contextualizer-self-review", 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 ntorga/agent-starter-kit --skill contextualizer-self-review -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ntorga/agent-starter-kit contextualizer-self-review --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ntorga/agent-starter-kit.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/contextualizer-self-review .opencode/skills/contextualizer-self-review && 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 "contextualizer-self-review" agent skill from https://github.com/ntorga/agent-starter-kit/tree/main/skills/contextualizer-self-review into .opencode/skills/contextualizer-self-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "contextualizer-self-review", 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.
contextualizer-self-reviewDeterministic self-evaluation rubric for Contextualizer — scored every run using the TRACE framework.
Contextualizer Self Review is an agent skill from ntorga/agent-starter-kit. Deterministic self-evaluation rubric for Contextualizer — scored every run using the TRACE framework.
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Education, covering Quizzes and assessments. The repository describes itself as: The scaffold for your multi-model, personalized Natural Language AI Harness (NLAH) . The licence is MIT.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 851e942. 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.
Contextualizer Self Review loads about 2.3k tokens when it runs. Until then it costs about 32 tokens; SKILL.md has 1,297 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 ntorga/agent-starter-kit at commit 851e942, republished under its MIT licence (© ntorga). 1,297 words, ~2,302 tokens.
.claude/skills/contextualizer-self-review/SKILL.md (or your agent's skills folder).Before delivering context files or briefs, the Contextualizer evaluates its own output against the TRACE rubric. Each letter is scored 0, 1, or 2 with evidence quoted from the rubric and cited from actual work. The total determines whether to deliver, rewrite, or abort.
Gather evidence. Honest self-review makes verification efficient. Accurate scorecards confirm fast; dishonest ones fail and re-run, wasting time and compute. Before scoring, run verification commands to gather proof. The examples below show common patterns — choose what provides the best evidence for your specific work.
Examples:
.context.md files have opening <context> tag with path and date, Summary, Constraints, Guidance sectionsFEATURE-MAP.md entries have feature name, flow steps with file paths and role descriptionstest -f <path> for directories/files mentioned in context — confirms claims are groundedfind . -type f | wc -l and find . -type d | wc -l to check project size against yield thresholdwc -l <context-files> — each context file should be shorter than the directory it describesThese are examples, not mandates. Choose commands that provide the strongest proof for your output.
Score each criterion. Read the TRACE rubric below. For each letter, assign a score of 0, 1, or 2. You must:
Output the Scorecard. Fill in the scorecard below. This is not internal reasoning — this is your deliverable checkpoint.
Apply the hard-fail rule. If any letter scores 0, do not deliver — go to step 5 immediately.
Determine action by total score:
Complete this before delivering. Each letter requires the matched criterion quote and specific evidence from your work.
Total: X/10 → Action: [DELIVER/FIX/RESTART]
Did I classify the task correctly and produce the right deliverable?
.context.md files AND FEATURE-MAP.md. Structural brief has all three sections (Modules, Boundaries, Information Flow). Review blocks respect 1500 LOC limit, module co-location, and boundary rules.Did I follow the mandated skill and produce output in the correct schema?
skills/context-maintenance/SKILL.md for full scan. Used ad-hoc format instead of the prescribed .context.md or FEATURE-MAP.md schema. Did not run the directory scan script when producing context files. Structural brief does not use the required three-section format.<context> tag missing path or date, feature flow step lacks "what happens here" description, or updated date touched without content change.skills/context-maintenance/SKILL.md end-to-end. .context.md files use exact schema: opening <context> tag with path and date, Summary, Constraints, Guidance sections. FEATURE-MAP.md uses exact schema: feature name, flow steps in order with file paths and role descriptions. Structural brief uses exact three-section format. Updated only drifted features in the existing map — did not rewrite it.Is every claim grounded in actual code? Nothing invented, nothing assumed.
.context.md that cannot be verified from the code itself. Added a feature to the map without tracing its full path through the codebase..context.md constraints verified from code itself. Every FEATURE-MAP.md entry traced end-to-end from entry point to output. No assumptions, no inference without evidence.Did I scope correctly — incremental update, yield when too big, respect boundaries?
FEATURE-MAP.md from scratch when it already existed. Project exceeds 200 files / 50 directories and no yield or coverage report was produced. Review blocks cross major architectural boundaries without tight coupling.FEATURE-MAP.md, untouched stable ones. Yield condition evaluated: project size checked, coverage gap reported if exceeded. Review blocks each under 1500 LOC, module files co-located, boundaries respected. Nothing left silently unprocessed.Can someone arriving cold orient from this output alone? Is it brief enough?
.context.md takes longer to read than the directory itself. Newcomer cannot determine what a directory does without reading the code. Feature map cannot be followed from entry point to output..context.md summary is too detailed or one feature flow step is unclear without inspecting the code. Structure present but one section reads like prose instead of a quick-reference list..context.md is brief: one-to-two sentence description, one-line-per-file summary, constraints and guidance only when needed. Feature map follows a straight path from entry to output — a newcomer can trace the flow without opening code. Structure over prose. Brevity check passes: output is shorter than the directory it describes.© ntorga, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/contextualizer-self-review of ntorga/agent-starter-kit.
Open the folder on GitHubat commit 851e942
Contextualizer Self Review 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 |
|---|---|---|---|---|---|---|
| Contextualizer Self Review this skillntorga/agent-starter-kit | 146 | — | ~2.3k | Automated safety check: Pass | MIT | |
| DeepTutor CLIHKUDS/DeepTutor | 41k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| AI Engineering Placement Quizrohitg00/ai-engineering-from-scratch | 66k | — | ~2k | Automated safety check: Pass | MIT | |
| Codebase to Coursezarazhangrui/codebase-to-course | 5.7k | — | ~4.4k | Automated safety check: Pass | None | |
| AI Engineering Phase Quizrohitg00/ai-engineering-from-scratch | 66k | — | ~2.1k | Automated safety check: Pass | MIT | |
| Scholar EvaluationK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~2.9k | Automated safety check: Notes | MIT |
HKUDS/DeepTutor
Teaches the agent to set up and run DeepTutor from the command line: chat and capabilities, knowledge bases, partners, memory, sessions, notebooks and the server or Web app.
rohitg00/ai-engineering-from-scratch
Runs a 10-question quiz across five areas to place a learner in the AI Engineering from Scratch curriculum, so they skip what they already know.
zarazhangrui/codebase-to-course
Turns a codebase into an interactive single-page HTML course for non-technical learners, with scroll modules, animated diagrams, quizzes and plain-English code translations.
rohitg00/ai-engineering-from-scratch
Quizzes you on a completed phase of the AI Engineering from Scratch course, taking a phase number or name and mapping it to that phase's directory.
K-Dense-AI/claude-scientific-writer
Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls.
guanyang/open-agent-hub
This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and…
ntorga/agent-starter-kit
Deterministic self-evaluation rubric for decision escalations — scored every run using the FRAME framework.
ntorga/agent-starter-kit
Long-term and session memory across sessions. An agent skill from ntorga/agent-starter-kit.
ntorga/agent-starter-kit
Builds the design tree for the grill — decisions mapped as nodes with dependencies, recommendations, and impact, pruned by path.
ntorga/agent-starter-kit
Grounds the grill's settled decisions in the codebase — annotates impl.md with file paths, signatures, reference files, test specs, and LOC; re-grounds the next epic after each landing.
ntorga/agent-starter-kit
Session startup — gitignore, auto-update, memory, rules, context, CLI config, and greet.
ntorga/agent-starter-kit
Browser inspection and interaction for verifying rendered web UI during development.
Categories
Deterministic self-evaluation rubric for Contextualizer — scored every run using the TRACE framework. Contextualizer Self Review is an agent skill from ntorga/agent-starter-kit. Deterministic self-evaluation rubric for Contextualizer — scored every run using the TRACE framework.
Contextualizer Self Review fits situations like: tasks that involve Quizzes and assessments.
Run `npx skills add ntorga/agent-starter-kit --skill contextualizer-self-review -a claude-code`. Or copy the skill folder (skills/contextualizer-self-review in ntorga/agent-starter-kit) into .claude/skills/contextualizer-self-review in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ntorga/agent-starter-kit --skill contextualizer-self-review -a codex`. Or copy the skill folder (skills/contextualizer-self-review in ntorga/agent-starter-kit) into .agents/skills/contextualizer-self-review 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 ntorga/agent-starter-kit --skill contextualizer-self-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/contextualizer-self-review, .gemini/skills/contextualizer-self-review, .github/skills/contextualizer-self-review and .opencode/skills/contextualizer-self-review in your project.
SKILL.md names no scripts, command-line tools or credentials: Contextualizer Self Review 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.
Contextualizer Self Review is published under the MIT 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.
Skills that share tags, products or a category with Contextualizer Self Review: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 66k stars), Codebase to Course (zarazhangrui/codebase-to-course, 5.7k stars) and AI Engineering Phase Quiz (rohitg00/ai-engineering-from-scratch, 66k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ntorga (a GitHub user) maintains it in ntorga/agent-starter-kit, which has 146 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on September 12, 2026.
Source: ntorga/agent-starter-kit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.