Borrowing it
Nothing to install: this file belongs to andrew-yangy/gru-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/andrew-yangy/gru-ai/main/.claude/skills/walkthrough/SKILL.mdgit clone --depth 1 https://github.com/andrew-yangy/gru-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/andrew-yangy/gru-ai/walkthrough)<a href="https://agentmods.dev/skills/andrew-yangy/gru-ai/walkthrough"><img src="https://agentmods.dev/badge/skills/andrew-yangy/gru-ai/walkthrough/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/andrew-yangy/gru-ai/walkthrough"><img src="https://agentmods.dev/badge/skills/andrew-yangy/gru-ai/walkthrough.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.03251 |
| Opus 5 | $0.00024 | $0.01625 |
| Sonnet 5 | $0.00010 | $0.00650 |
| Haiku 4.5 | $0.00005 | $0.00325 |
Grade A, and why
walkthrough scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 371 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Walkthrough — Cognitive Walkthrough
Role Resolution
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
- A specific scenario name (e.g.,
ceo-runs-directive) → run just that one all→ run all standing scenarios- A free-text scenario description (e.g.,
"seller wants to see competitor prices") → ad-hoc walkthrough - Empty → list available scenarios and ask which to run
Step 1: Load Scenarios
If $ARGUMENTS is a scenario name or "all":
Read standing scenarios from .context/lessons/scenarios.md.
Each scenario has:
- Name: slug identifier
- Actor: who is performing the action (CEO, seller, shopper, developer)
- Trigger: what starts the flow ("CEO types /directive improve-security")
- Goal: what the actor wants to achieve
- Critical path: the steps that MUST work for the scenario to succeed
If all, load all scenarios. If a specific name, load just that one.
If $ARGUMENTS is free text:
Treat it as an ad-hoc scenario. Spawn the CPO to formalize it:
You are the CPO. The CEO described a user scenario informally:
"{$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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 371 lines · 48 tokens per session scan A 98d5cbad5625
walkthrough is a skill published in the GitHub repository andrew-yangy/gru-ai (153 stars, last pushed 6mo ago), licensed MIT. It adds 48 tokens to every session and 3,251 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
test-reporting
Run the Level 2 dummy agent integration test suite and produce a detailed HTML report with per-test input → outcome analysis.
develop-web-game
Use when Codex is building or iterating on a web game (HTML/JS) and needs a reliable development + testing loop: implement small changes, run a Playwright-based test script with short input bursts and intentional pauses, inspect screenshots/text, and review console errors with rendergametotext.
gsd-verify-work
Validate built features through conversational UAT.
gsd-add-tests
Generate tests for a completed phase based on UAT criteria and implementation.
gsd-audit-uat
Cross-phase audit of all outstanding UAT and verification items.
11-browser-qa
Run post-review browser QA and produce short named videos for a locked happy path and sourced browser edge cases. Use when the user wants concise reviewer evidence for a web journey. Not for API, CLI, automated tests, diff review, or application fixes.