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 tobihagemann/turbo --skill investigategit clone --depth 1 https://github.com/tobihagemann/turboWrote 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/tobihagemann/turbo/investigate)<a href="https://agentmods.dev/skills/tobihagemann/turbo/investigate"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/investigate/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/tobihagemann/turbo/investigate"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/investigate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00107 | $0.02350 |
| Opus 5 | $0.00053 | $0.01175 |
| Sonnet 5 | $0.00021 | $0.00470 |
| Haiku 4.5 | $0.00011 | $0.00235 |
Grade A, and why
investigate 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 12d 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigate
Systematic methodology for finding the root cause of bugs, failures, and unexpected behavior. Cycle through characterize-isolate-hypothesize-test steps, with oracle escalation for hard problems. Diagnose the root cause — do not apply fixes.
Optional: $ARGUMENTS contains the problem description or error message.
Step 1: Characterize
Gather the symptom and establish what is actually happening:
- Collect evidence — error message, stack trace, test output, log entries, or user description of unexpected behavior
- Classify the problem type:
| Signal | Type |
|---|---|
| Stack trace / exception | Runtime error |
| Test assertion failure | Test failure |
| Compilation / bundler / build error | Build failure |
| Type checker error (tsc, mypy, pyright) | Type error |
| Slow response / high CPU / memory growth | Performance |
| "It does X instead of Y" / no error | Unexpected behavior |
- Establish reproduction — run the failing command, test, or operation. If the problem cannot be reproduced (intermittent, environment-specific), document the constraints and proceed with historical evidence.
Record the exact reproduction command and its output for verification. For intermittent or long-running reproductions, use the Monitor tool to tail logs filtered for relevant signals (errors, stack traces, specific identifiers) so failures surface live while you work.
Step 2: Isolate
Narrow from "something is wrong" to "the problem is in this area." Read references/problem-type-playbooks.md for type-specific first moves and tool sequences.
Git Archeology
For all problem types, check what changed recently near the failure point:
git log --oneline -20 -- <file>
git blame -L <start>,<end> <file>
If a known-good state exists (e.g., "this worked yesterday"), consider git bisect to pinpoint the breaking commit.
Upstream Issue Search
When the failure surfaces inside a third-party dependency, search its issue tracker for a distinctive string from the error before reading deeper into the dependency's code. An issue whose symptom matches often names the cause and the fix outright. Carry a match forward as a ranked hypothesis and test it.
What ships with it
1 file 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.
- 12d ago First seen · 173 lines · 107 tokens per session scan A 7dc7763929a5
investigate is a skill published in the GitHub repository tobihagemann/turbo (402 stars, last pushed 3d ago), licensed MIT. It adds 107 tokens to every session and 2,350 once invoked, about $0.0005 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.
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