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-investigationgit 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-investigation)<a href="https://agentmods.dev/skills/decodingai-magazine/building-a-coding-agent-from-scratch-course/kitaru-investigation"><img src="https://agentmods.dev/badge/skills/decodingai-magazine/building-a-coding-agent-from-scratch-course/kitaru-investigation/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-investigation"><img src="https://agentmods.dev/badge/skills/decodingai-magazine/building-a-coding-agent-from-scratch-course/kitaru-investigation.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 | $0.00147 | $0.04653 |
| Opus 5 | $0.00073 | $0.02327 |
| Sonnet 5 | $0.00029 | $0.00931 |
| Haiku 4.5 | $0.00015 | $0.00465 |
Grade A, and why
kitaru-investigation 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 yesterday.
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-investigation — 95% identical, 32 lines differ
How it starts
The opening of the file, as written. The whole thing — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kitaru investigation
Treat this skill as Kitaru's evidence-led front door for real agents and traces.
Guide one continuous journey from the evidence the user has to a reviewed
behavior and, when they want to test a change, the
kitaru-replay-experiment skill.
Core contract
- Treat the human as the judge. Select, summarize, organize, and compile evidence; never turn an agent suggestion into a human label.
- Preserve durable Kitaru state. Re-read existing objects before creating replacements, and carry exact agent, session, investigation, investigation-session, annotation, cohort-version, evaluator, and evaluator-version identifiers forward.
- Separate observed behavior from desired behavior. A trace records what happened, not what should have happened or whether the external outcome was correct.
- Distinguish agent behavior, external dependency behavior, product purpose, and independent outcome evidence.
- Use open observations before proposing a taxonomy. Deterministic signals may select sessions; they do not judge them.
- Explain remote writes and paid or live execution before running them. Ask for one proportional confirmation at the point of action.
- Prefer native Kitaru MCP operations when available. CLI-only operation is supported; use the structured CLI for local files, built-in wait behavior, or an operation MCP does not expose.
- Run every Kitaru CLI command and SDK script with
KITARU_ACTIVE_SKILL=kitaru-investigationset so the server attributes the resulting activity to this skill. - 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. - Stop at a useful durable checkpoint when a required source, payload, permission, worker, product contract, or UI capability is unavailable.
Keep the experience light
- Lead with the current state and one next useful action.
- Use ordinary language for user decisions. Keep internal labels such as
trace-first,cold-start, question keys, and selectors out of the lead. - Ask for one meaningful judgment at a time. Do not turn setup into a long questionnaire.
- Prefer short prose during active review. Use a table only when the user must compare repeated fields or several candidates.
- Summarize structured output instead of dumping JSON. Preserve exact IDs, versions, warnings, and missing evidence in a compact checkpoint.
- Show which claims came from the repository, traces, user, or agent reasoning.
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.
- yesterday First seen · 442 lines · 147 tokens per session scan A aa0517629432
kitaru-investigation is a skill published in the GitHub repository decodingai-magazine/building-a-coding-agent-from-scratch-course (383 stars, last pushed yesterday), licensed Apache-2.0. It adds 147 tokens to every session and 4,653 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.
Other skills, from other repositories
review
Review a change for correctness, security, and missing tests.
hooks
Create and manage lifecycle hooks that vixd fires automatically on agent-loop events (a tool about to run, a prompt submitted, a thread starting, a turn finishing). Use when the user wants to enforce a rule, block or rewrite a tool call, validate prompts, auto-format, notify, or react to what the agent does.
jobs
Create and manage scheduled jobs (cron tasks, reminders, heartbeat checks, polling watchers) that vixd runs automatically. Use when the user asks to schedule, automate, monitor, watch, or be reminded about something.
workflow
Author and edit vix workflows — declarative multi-step pipelines (agent/bash/tool/if/fanout/fanin nodes) stored in config/workflow.json or embedded inline in a job/hook. Use when the user asks to create, modify, or understand a workflow, or to fan work out across many items and join the results.
review-pr
Deeply review a GitHub pull request.
vix-help
Answer questions about vix itself — the TUI (keybindings, tabs, threads, slash commands), configuration (settings.json, deny lists, permissions), agents & tools, models & providers, jobs, hooks, skills, workflows, MCP servers, the brain/code intelligence, headless/CLI usage, and installation. Use whenever the user…