MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Cognitive walkthrough — simulate real user scenarios against the current system to find gaps between ideal and actual.
$ npx skills add andrew-yangy/gru-ai --skill walkthrough -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install andrew-yangy/gru-ai walkthrough --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/andrew-yangy/gru-ai.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/walkthrough .claude/skills/walkthrough && 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 "walkthrough" agent skill from https://github.com/andrew-yangy/gru-ai/tree/main/.claude/skills/walkthrough into .claude/skills/walkthrough/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "walkthrough", 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/andrew-yangy/gru-ai/tree/main/.claude/skills/walkthroughType 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 andrew-yangy/gru-ai --skill walkthrough -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install andrew-yangy/gru-ai walkthrough --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/andrew-yangy/gru-ai.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/walkthrough .agents/skills/walkthrough && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "walkthrough" agent skill from https://github.com/andrew-yangy/gru-ai/tree/main/.claude/skills/walkthrough into .agents/skills/walkthrough/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "walkthrough", 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 andrew-yangy/gru-ai --skill walkthrough -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install andrew-yangy/gru-ai walkthrough --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/andrew-yangy/gru-ai.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/walkthrough .cursor/skills/walkthrough && 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 "walkthrough" agent skill from https://github.com/andrew-yangy/gru-ai/tree/main/.claude/skills/walkthrough into .cursor/skills/walkthrough/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "walkthrough", 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/andrew-yangy/gru-ai.git --path .claude/skills/walkthrough--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 andrew-yangy/gru-ai --skill walkthrough -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install andrew-yangy/gru-ai walkthrough --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/andrew-yangy/gru-ai.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/walkthrough .gemini/skills/walkthrough && 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 "walkthrough" agent skill from https://github.com/andrew-yangy/gru-ai/tree/main/.claude/skills/walkthrough into .gemini/skills/walkthrough/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "walkthrough", 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 andrew-yangy/gru-ai walkthroughInstalls 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 andrew-yangy/gru-ai --skill walkthrough -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/andrew-yangy/gru-ai.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/walkthrough .github/skills/walkthrough && 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 "walkthrough" agent skill from https://github.com/andrew-yangy/gru-ai/tree/main/.claude/skills/walkthrough into .github/skills/walkthrough/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "walkthrough", 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 andrew-yangy/gru-ai --skill walkthrough -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install andrew-yangy/gru-ai walkthrough --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/andrew-yangy/gru-ai.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/walkthrough .opencode/skills/walkthrough && 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 "walkthrough" agent skill from https://github.com/andrew-yangy/gru-ai/tree/main/.claude/skills/walkthrough into .opencode/skills/walkthrough/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "walkthrough", 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.
walkthroughCognitive walkthrough — simulate real user scenarios against the current system to find gaps between ideal and actual.
Walkthrough is an agent skill from andrew-yangy/gru-ai. Cognitive walkthrough — simulate real user scenarios against the current system to find gaps between ideal and actual. Takes an optional scenario name or 'all' to run standing scenarios. Run after major directives or periodically as a reality check.
Its SKILL.md is about 3.4k 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 Agent Workflows. The repository describes itself as: Autonomous AI agent team for one-man companies. Context engineering + harness engineering drive a pipeline that brainstorms, builds, reviews, and ships. The licence is MIT.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 8fba479. 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.
Walkthrough loads about 3.4k tokens when it runs. Until then it costs about 65 tokens; SKILL.md has 679 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 andrew-yangy/gru-ai at commit 8fba479, republished under its MIT licence (© andrew-yangy). 679 words, ~3,351 tokens.
.claude/skills/walkthrough/SKILL.md (or your agent's skills folder).Read .claude/agent-registry.json to map roles to agent names. Use each agent's id as the subagent_type when spawning. The CPO designs the ideal experience; the CTO traces the actual implementation.
Simulate user scenarios against the current system. Find what's broken, missing, or surprising.
The pattern: For each scenario, design what SHOULD happen (ideal), trace what DOES happen (actual), report the gaps.
Arguments: $ARGUMENTS
ceo-runs-directive) → run just that oneall → run all standing scenarios"seller wants to see competitor prices") → ad-hoc walkthroughRead standing scenarios from .context/lessons/scenarios.md.
Each scenario has:
If all, load all scenarios. If a specific name, load just that one.
Treat it as an ad-hoc scenario. Spawn the CPO to formalize it:
You are the CPO. The CEO described a user scenario informally:
"{$ARGUMENTS}"
Formalize it into this structure:
{
"name": "slug-name",
"actor": "who is doing this",
"trigger": "what starts the flow",
"goal": "what the actor wants to achieve",
"critical_path": [
"Step 1: what should happen first",
"Step 2: what should happen next",
...
],
"success_criteria": "how do you know the scenario succeeded"
}
Think from the ACTOR's perspective, not the system's. What does the actor expect at each step? What would surprise or frustrate them?
CRITICAL OUTPUT FORMAT: First character must be `{`, last must be `}`. JSON only.Read .context/lessons/scenarios.md and list available scenarios:
Available scenarios:
1. ceo-runs-directive — CEO issues a directive, wants it handled without blocking
2. ceo-morning-review — CEO opens dashboard, wants to know what happened overnight
3. ...
Which scenario to walk through? (or describe a new one)Use AskUserQuestion with the scenario names as options.
For each scenario, spawn the CPO to design the ideal experience — what SHOULD happen if everything worked perfectly.
The CPO receives:
.context/vision.md — so the ideal aligns with the north star.context/preferences.md — CEO expectationsYou are the CPO. You are designing the IDEAL user experience for this scenario. Don't look at the current implementation — design from scratch what the perfect flow would be.
SCENARIO:
- Actor: {actor}
- Trigger: {trigger}
- Goal: {goal}
For each step of the critical path, describe:
1. What the actor does
2. What the system should do in response
3. What the actor sees/experiences
4. How long it should take (instant / seconds / minutes / async)
5. What would frustrate the actor at this step
Then describe the END STATE: what does "success" look like from the actor's perspective?
Think like a product designer, not an engineer. The actor doesn't care about checkpoints, worktrees, or JSON schemas. They care about: did the thing work? Was it fast? Did I have to babysit it?
CRITICAL OUTPUT FORMAT: First character must be `{`, last must be `}`. JSON only.
{
"scenario": "{name}",
"ideal_flow": [
{
"step": 1,
"actor_action": "what the actor does",
"system_response": "what should happen",
"actor_experience": "what they see/feel",
"timing": "instant | seconds | minutes | async",
"frustration_risk": "what could annoy the actor here"
}
],
"end_state": "what success looks like",
"key_expectations": ["the non-negotiable things the actor expects"]
}For each scenario, spawn the CTO to trace what ACTUALLY happens in the current system. The CTO reads code, config, and skill files to follow the real execution path.
The CTO receives:
.context/lessons/ topic files — known issues.context/preferences.mdYou are the CTO. You are tracing what ACTUALLY happens in the current system for this scenario. Read the real code and config — don't guess.
SCENARIO:
- Actor: {actor}
- Trigger: {trigger}
- Goal: {goal}
- Critical path: {steps}
IDEAL FLOW (from the CPO):
{CPO's ideal_flow JSON}
For each step of the ideal flow, trace what the current system actually does:
1. Read the relevant files (SKILL.md, agent files, code)
2. Follow the execution path step by step
3. Note where reality matches the ideal
4. Note where reality DIVERGES from the ideal
5. Note where the system does NOTHING (missing functionality)
Be thorough. Grep for entry points, read the actual instructions, trace the branching logic. Don't assume — verify.
### Doc Consistency Checks
After tracing the execution flow, check the pipeline's internal
documentation for consistency. These checks catch drift between docs
that causes real pipeline failures — fields referenced in one file but
undefined in another, prompt templates injecting stale field names,
validation scripts that don't enforce what the docs promise.
Run these three checks by reading actual file contents (use Grep and
Read). Do NOT guess from memory.
**A. Cross-reference verification.** For each pipeline step doc in
`.claude/skills/directive/docs/pipeline/`, check that any
directive.json or project.json field it references actually exists in
the corresponding schema doc under
`.claude/skills/directive/docs/reference/schemas/` (especially
`directive-json.md` and `plan-schema.md`). Example: if
`09-execute-projects.md` reads `directive.planning.coo_plan`, confirm
`directive-json.md` defines `planning.coo_plan`.
**B. Schema-to-template alignment.** For each prompt template in
`.claude/skills/directive/docs/reference/templates/`, check that the
fields it injects (placeholders like `{field_name}` or references to
JSON paths) match the schema definitions in
`.claude/skills/directive/docs/reference/schemas/`. Example: if
`planner-prompt.md` injects `{audit.risk_areas}`, confirm
`audit-output.md` defines `risk_areas`.
**C. Validation script coverage.** For each validation script in
`.claude/hooks/validate-*.sh`, check that the fields it validates
match what the pipeline docs and schemas claim are enforced. Example:
if `07-plan-approval.md` says "the gate validates `projects` array
exists", confirm `validate-gate.sh` actually checks for that field.
Also check the inverse — if a doc says a field is "required" or
"enforced", a validation script should check it.
Record every discrepancy. Omit checks that pass — only report
mismatches.
CRITICAL OUTPUT FORMAT: First character must be `{`, last must be `}`. JSON only.
{
"scenario": "{name}",
"actual_flow": [
{
"ideal_step": 1,
"ideal_expectation": "what the CPO said should happen",
"actual_behavior": "what the system actually does",
"status": "match | diverge | missing | broken",
"evidence": "file:line or config entry that proves this",
"notes": "explanation of the gap, if any"
}
],
"gaps_found": [
{
"id": "gap-slug",
"severity": "critical | major | minor | cosmetic",
"type": "missing | broken | wrong | slow | confusing",
"description": "what's wrong",
"ideal": "what should happen",
"actual": "what does happen",
"evidence": "file:line",
"suggested_fix": "how to close the gap"
}
],
"doc_consistency": {
"cross_ref_issues": [
{
"source_file": "pipeline doc that references the field",
"references": "the field or path referenced",
"expected_in": "schema doc where it should be defined",
"issue": "missing | renamed | wrong_path"
}
],
"schema_drift": [
{
"template_file": "prompt template that injects the field",
"injects": "field name or path the template uses",
"schema_file": "schema doc that should define it",
"issue": "field missing from schema | field renamed | type mismatch"
}
],
"validation_gaps": [
{
"script": "validate-*.sh script name",
"doc_claims": "what the pipeline doc says is enforced",
"doc_source": "pipeline doc making the claim",
"issue": "script does not check this | script checks stale field name"
}
]
},
"working_well": ["things that match the ideal — acknowledge what's good"]
}After all scenarios are traced, consolidate the findings:
doc_consistency findings across
all scenario traces. Deduplicate cross_ref_issues, schema_drift, and
validation_gaps that appear in multiple traces (same source_file +
references pair, same template_file + injects pair, or same script +
doc_claims pair). Flag any issue that appears in 3+ traces as systemic
drift. Keep one canonical entry per unique issue with a found_in
list of scenario names.# Walkthrough Report — {date}
## Scenarios Walked: {count}
### {Scenario Name}
**Actor**: {actor} | **Goal**: {goal}
**Ideal vs Actual:**
| Step | Ideal | Actual | Status |
|------|-------|--------|--------|
| 1 | {ideal} | {actual} | ✅ match / ⚠️ diverge / ❌ missing |
| 2 | ... | ... | ... |
**Gaps Found: {count}**
- [{severity}] **{description}** — {type}
Ideal: {what should happen}
Actual: {what does happen}
Fix: {suggested fix} ({effort})
**Working Well:**
- {things that matched the ideal}
(repeat per scenario)
## Systemic Gaps (appear in 2+ scenarios)
- **{gap}** — found in: {scenario list}
(if doc_consistency findings exist across any scenario trace, include this section)
## Doc Consistency
Issues found by cross-checking pipeline docs, schemas, templates, and
validation scripts. These cause silent pipeline failures when docs drift
out of sync.
### Cross-Reference Mismatches
| Source File | References | Expected In | Issue |
|-------------|-----------|-------------|-------|
| {source_file} | {references} | {expected_in} | {issue} |
### Schema-Template Drift
| Template File | Injects | Schema File | Issue |
|---------------|---------|-------------|-------|
| {template_file} | {injects} | {schema_file} | {issue} |
### Validation Coverage Gaps
| Script | Doc Claims | Doc Source | Issue |
|--------|-----------|------------|-------|
| {script} | {doc_claims} | {doc_source} | {issue} |
(omit any subsection whose table would be empty)
## Summary
- Total gaps: {count} ({critical}, {major}, {minor})
- Scenarios fully passing: {count}/{total}
- Top 3 fixes by impact: {list}Then ask the CEO:
Write the full report to .context/reports/walkthrough-{date}.md
If gaps were approved as a directive, create it in .context/directives/.
If .context/lessons/scenarios.md doesn't exist, create it with starter scenarios on first run. The CEO and team add scenarios over time as new flows become important.
| Situation | Action |
|---|---|
| The CPO can't formalize ad-hoc scenario | Ask CEO to clarify the scenario |
| The CTO can't find the entry point for a step | Mark as "missing — no implementation found" |
| A scenario has no gaps | Report it as passing — this is good news |
| scenarios.md doesn't exist | Create it with starter scenarios, then run |
© andrew-yangy, 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 .claude/skills/walkthrough of andrew-yangy/gru-ai.
Open the folder on GitHubat commit 8fba479
Walkthrough 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 |
|---|---|---|---|---|---|---|
| Walkthrough this skillandrew-yangy/gru-ai | 155 | — | ~3.4k | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 63 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Hook Development for Claude Code Pluginsanthropics/claude-plugins-official | 38k | 10 repos | ~4.1k | Automated safety check: Notes | Apache-2.0 | |
| Using Superpowersfarm-fe/farm | 5.6k | 35 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Executing Plans Inlineobra/superpowers | 297k | 2 repos | ~5.1k | Automated safety check: Pass | MIT | |
| Skill CreatorAzure/azqr | 795 | 89 repos | ~8.2k | Automated safety check: Pass | Apache-2.0 |
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
anthropics/claude-plugins-official
Explains how to write Claude Code plugin hooks, both prompt-based checks and bash commands, for events such as PreToolUse, Stop and SessionStart.
farm-fe/farm
A skill your agent uses when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
obra/superpowers
Has the agent carry out an implementation plan itself, task by task in the current session, keeping a ledger, proving each step with a test and ending with one whole-branch review.
Azure/azqr
Create new skills, modify and improve existing skills, and measure skill performance.
anthropics/claude-plugins-official
Explains how to write agents for Claude Code plugins: the markdown file with YAML frontmatter, trigger descriptions, model and color settings, and system prompt design.
andrew-yangy/gru-ai
Provides comprehensive code review guidance for React 19, Vue 3, Rust, TypeScript, Java, Python, and C/C++.
andrew-yangy/gru-ai
Full website SEO audit with parallel subagent delegation. An agent skill from andrew-yangy/gru-ai.
andrew-yangy/gru-ai
Structured brainstorm — from quick Socratic refinement to full C-suite strategy sessions.
andrew-yangy/gru-ai
Internal codebase and operations health check — the CTO scans technical health, the COO checks operational health.
andrew-yangy/gru-ai
CEO dashboard with progressive disclosure — 3 tiers: headline (5 lines, default), summary (per-goal detail), deep (full weekly analysis).
andrew-yangy/gru-ai
Pipeline end-to-end smoke test -- creates a trivial directive, runs it through /directive, validates every pipeline step, and reports pass/fail with evidence.
Categories
Cognitive walkthrough — simulate real user scenarios against the current system to find gaps between ideal and actual. Walkthrough is an agent skill from andrew-yangy/gru-ai. Cognitive walkthrough — simulate real user scenarios against the current system to find gaps between ideal and actual.
Walkthrough fits situations like: agent Workflows work in your project.
Run `npx skills add andrew-yangy/gru-ai --skill walkthrough -a claude-code`. Or copy the skill folder (.claude/skills/walkthrough in andrew-yangy/gru-ai) into .claude/skills/walkthrough in your project. Claude Code loads it when a task matches its description.
Run `npx skills add andrew-yangy/gru-ai --skill walkthrough -a codex`. Or copy the skill folder (.claude/skills/walkthrough in andrew-yangy/gru-ai) into .agents/skills/walkthrough 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 andrew-yangy/gru-ai --skill walkthrough -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/walkthrough, .gemini/skills/walkthrough, .github/skills/walkthrough and .opencode/skills/walkthrough in your project.
SKILL.md names no scripts, command-line tools or credentials: Walkthrough 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.
Walkthrough is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.4k tokens (SKILL.md is roughly 13k 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 Walkthrough: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 297k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
andrew-yangy (a GitHub user) maintains it in andrew-yangy/gru-ai, which has 155 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on March 11, 2026.
Source: andrew-yangy/gru-ai on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.