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 automagik-dev/genie --skill reportgit clone --depth 1 https://github.com/automagik-dev/genieWrote 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/automagik-dev/genie/report)<a href="https://agentmods.dev/skills/automagik-dev/genie/report"><img src="https://agentmods.dev/badge/skills/automagik-dev/genie/report/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/automagik-dev/genie/report"><img src="https://agentmods.dev/badge/skills/automagik-dev/genie/report.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 | $0.00030 | $0.01675 |
| Opus 5.5 | $0.00012 | $0.00670 |
| Sonnet 5.5 | $0.00006 | $0.00335 |
| Haiku 4.5 | $0.00003 | $0.00168 |
Grade A, and why
report 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 1mo 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Report
Investigate; never fix. The deliverable is a diagnosis another agent can act on without reproducing the failure. Source edits belong to fix; creating an issue is a separate external write that happens only when the request asks for it or the user confirms it.
When to use
- A failure exists and the cause is unknown, or the error points nowhere obvious.
revieworfixneeds a root cause before spending a repair attempt.- Someone wants a self-contained bug report, with or without a GitHub issue.
- A QA criterion in a wish failed after merge; map the failure to the criterion it violates before tracing.
Investigate
- Collect symptoms: the description (required), plus error text, stack traces, logs, URL, and expected versus actual behavior when offered. Ask only for what the investigation needs.
- Build a red loop before forming a theory: name one command that fails right now for the reason under investigation, run it at least once, and keep its output. The loop must be red-capable (it drives the failing path and asserts the reported symptom, not merely "something errored"), deterministic, fast, and runnable unattended. Build it from whatever is cheapest that still reaches the failure: a failing test at any seam, a request script against a running service, a CLI invocation diffed against known-good output, a headless browser script, a replay of a captured trace, a bisection run over the history between a known-good and a known-bad state, a differential loop that puts one input through two versions, configurations, providers or datasets, a property or fuzz loop when the wrong output only appears across a broad input space, or a throwaway harness that boots the smallest slice of the system that still fails. Tighten it before trusting it — sharper assertion, pinned time and seed, fewer seconds. When the failure refuses to fire every time, raise the reproduction rate rather than chase determinism: repeat the trigger, run copies in parallel, add load, narrow the timing window, pin the clock, and seed every source of randomness. Say the rate out loud, because it decides what happens next — a failure that fires about half the time is debuggable, and one that fires once in a hundred runs is a measurement problem to solve before it is a bug to trace. If you catch yourself reading code to build a theory before this command exists, stop; jumping to a hypothesis is the failure this gate prevents. When no loop can be built at all, say so, list what was tried, and name what would unblock it (access to an environment that reproduces, a redacted capture, permission to instrument) rather than hypothesizing without one.
- Minimize the reproduction: with the loop red, cut one thing at a time — an input, a caller, a configuration value, a data row, a step — and re-run after every cut, keeping only what the failure needs. You are done when removing anything that remains turns the loop green. What survives is the smallest true statement of the bug, and it is usually the regression test
fixshould land. - Trace: reproduce, hypothesize, and isolate the root cause with read-only tools: search and non-mutating commands only, no edits, staging, commits, or publication. Investigate directly when one bounded investigation suffices; delegate a read-only
scoutthrough the runtime's native delegation surface when independent searches can run in parallel or the investigation needs isolated context, giving it the symptoms, relevant files, and the report format below, and steering it with follow-up messaging rather than starting a duplicate. If the failure cannot be reproduced, the report says so. Show 3-5 ranked falsifiable hypotheses before testing any of them — each stating what change would make the failure disappear or worsen — and then test one variable at a time against the loop. Generate hypotheses cheaply from code in this repository that does the same job and works: list every difference from the failing path, however small, and treat "that cannot matter" as an untested assumption rather than a conclusion. - Instrument without mutating what you are investigating: keep probes in scratch files outside the working tree. When a temporary in-tree probe is genuinely unavoidable, tag every added line with one unique marker such as
[DEBUG-a4f2], remove them all before handing off, and prove the tree is clean withgit status --porcelain. Instrumentation is never a fix, and an untagged probe is a leak. - Compile: every statement traces to tool output from this investigation. Include the supporting evidence the project already offers — a browser, console or network capture where a URL or dev server exists, recent related errors from monitoring the project actually configures — and where expected evidence could not be captured, say so with the reason. Never present a planned capture as evidence.
What ships with it
2 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.
- 1mo ago Changed 6d52505f5050
- 1mo ago Changed · +2 lines a9ea04f91d55
- 1mo ago Changed · -50 lines · -4 tokens per session eaaf51457b98
- 1mo ago First seen · 104 lines · 34 tokens per session scan A e28e69693d8d
report is a skill published in the GitHub repository automagik-dev/genie (345 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 1,675 once invoked, about $0.0001 per session on Opus 5.5. 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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root-cause-analysis
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systematic-debugging
Methodical bug investigation and root cause analysis.