Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add decodingai-magazine/building-a-coding-agent-from-scratch-course --skill kitaru-guided-tourgit clone --depth 1 https://github.com/decodingai-magazine/building-a-coding-agent-from-scratch-courseWrote 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/decodingai-magazine/building-a-coding-agent-from-scratch-course/kitaru-guided-tour)<a href="https://agentmods.dev/skills/decodingai-magazine/building-a-coding-agent-from-scratch-course/kitaru-guided-tour"><img src="https://agentmods.dev/badge/skills/decodingai-magazine/building-a-coding-agent-from-scratch-course/kitaru-guided-tour/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/decodingai-magazine/building-a-coding-agent-from-scratch-course/kitaru-guided-tour"><img src="https://agentmods.dev/badge/skills/decodingai-magazine/building-a-coding-agent-from-scratch-course/kitaru-guided-tour.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 | $0.00148 | $0.03148 |
| Opus 5 | $0.00074 | $0.01574 |
| Sonnet 5 | $0.00030 | $0.00630 |
| Haiku 4.5 | $0.00015 | $0.00315 |
Grade A, and why
kitaru-guided-tour 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 2d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- kitaru-guided-tour — 92% identical, 63 lines differ
How it starts
The opening of the file, as written. The whole thing — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kitaru guided tour
Deliver an AHA before teaching the complete method. Use the public Kitaru returns-agent template to move from recorded traces, through a short prepared frontend review and reusable evaluator, to one bounded experiment result.
Experience contract
- Assume the user has not read the template agent and does not yet know how Kitaru works. Explain each example from the evidence visible in the review, translate internal names into plain language, and never require missing code or product context to understand a question.
- Act as a friendly guide, not an invisible automation runner. At each meaningful transition, explain what the tour is doing, why that step matters, what Kitaru concept it demonstrates, and what the user will be able to see or do next. Keep routine commands in the background.
- If standing user instructions strongly prefer terse or explanation-free responses, surface the tension before beginning the tour. Ask once: “This guided tour works best if I briefly explain each new concept and why each step matters. May I use a little more explanation than usual during the tour, while keeping routine commands and status updates compact?” If the user agrees, treat that answer as a tour-scoped clarification of their preferred style, not permission to ignore unrelated or higher-priority instructions. If they decline, remain concise but still explain the minimum needed to understand each checkpoint.
- Use names that already exist in Kitaru when naming product objects. Do not invent labels for steps, summaries, or collections, and do not present an internal implementation detail as a Kitaru concept. Prefer a plain sentence such as “Here is what the experiment will do” over “replay run card.”
- Lead with what the user is about to discover, not installation or evaluation terminology.
- Teach only the concept needed for the current action. Explain deeper Kitaru objects after the user has experienced why they matter.
- Prepare useful observations and exact evidence anchors for the reviewer. Tell them once that these are agent-prepared notes and their verdict is the human judgment.
- Use the Kitaru frontend three times: for the investigation review, the cohort and evaluator results, and the completed experiment run. Give direct links, explain what the user is looking at, then pause until they return. Do not interrupt the tour with frontend visits for routine objects.
- Keep the first tour to three sessions: one consequential problem, one subtle evidence-reading lesson, and one acceptable counterexample.
- Reach a useful evaluator result without regenerating traces or making a paid model call. Continue into one bounded replay, but briefly explain the proposed run and ask before creating the experiment or starting paid or live execution.
- Before a model-backed replay, verify without exposing secrets that the worker runtime has the required provider credential. If availability cannot be verified, ask the user to configure it and restart the worker, then stop before creating the experiment or starting its run.
- Start or restart a user-controlled worker with
--concurrency 10. UseKITARU_WORKER_CONCURRENCY=10only when the launch surface exposes worker settings through environment variables instead of CLI options. - Preserve the user's healthy selected Kitaru server, whether local or cloud. Do not switch servers merely because the public template documents a local quickstart.
- Resume durable agents, imports, sessions, exact-match annotations, cohorts, evaluator versions, experiments, and runs before creating replacements. Resume an investigation and its verdicts only when this conversation holds its exact ID or the user explicitly identifies it as their review.
- Prefer one combined approval for the clearly previewed tutorial writes. Do not turn the tour into a sequence of permission prompts.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 269 lines · 148 tokens per session scan A 47dfc155c55c
kitaru-guided-tour is a skill published in the GitHub repository decodingai-magazine/building-a-coding-agent-from-scratch-course (387 stars, last pushed yesterday), licensed Apache-2.0. It adds 148 tokens to every session and 3,148 once invoked, about $0.0007 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 2026-09-09.
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