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 buzzer-re/Rikugan --skill generic-regit clone --depth 1 https://github.com/buzzer-re/RikuganWrote 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/buzzer-re/rikugan/generic-re)<a href="https://agentmods.dev/skills/buzzer-re/rikugan/generic-re"><img src="https://agentmods.dev/badge/skills/buzzer-re/rikugan/generic-re/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/buzzer-re/rikugan/generic-re"><img src="https://agentmods.dev/badge/skills/buzzer-re/rikugan/generic-re.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 | $0.00016 | $0.00653 |
| Opus 5 | $0.00008 | $0.00327 |
| Sonnet 5 | $0.00003 | $0.00131 |
| Haiku 4.5 | $0.00002 | $0.00065 |
Grade A, and why
General Reverse Engineering 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 9d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task: General Reverse Engineering. You are analyzing a binary to understand its functionality, architecture, or behavior. No assumption about maliciousness.
Approach
Build a mental map of the binary's structure. Start at the entry point or user-specified function. Name functions as you understand them — each rename makes the next function easier to read. Focus on what the user is interested in, not exhaustive coverage.
Workflow
get_binary_info— format, architecture, size, function countlist_imports+list_exports— understand the binary's interface (batch these)- Start at the function of interest (or entry if exploring)
decompile_function→ understand →rename_function/rename_variable→ follow call chains- Use
xrefs_toandxrefs_fromto trace data and code references - Build up a picture of the binary's modules, data structures, and control flow
Call Graph Strategy
Use xref tools BEFORE decompiling for exploration — they're cheaper:
function_xrefson entry → map top-level subsystems without decompiling everythingxrefs_toon interesting imports → find which functions use specific APIs- Decompile only the nodes you actually need to understand
- After understanding a function's purpose, check its callers to propagate context upward
Depth guidance:
- Immediate callers/callees: quick orientation
- 2 levels: neighborhood — usually sufficient
- 3+ levels: subsystem mapping — only for deep dives
Domain-Specific Tips
Libraries/frameworks: Focus on exported functions and their calling conventions. Use list_exports to map the public API.
Drivers/kernel modules: Identify dispatch routines, IOCTL handlers, initialization. Consider using /driver-analysis for Windows drivers.
Proprietary formats: Trace the parsing code. Use create_struct and suggest_struct_from_accesses to reconstruct data structures. Apply with apply_struct_to_address.
Firmware/embedded: Check for known library signatures in function prologues. Map memory-mapped I/O regions via list_segments.
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.
- 9d ago First seen · 61 lines · 16 tokens per session scan A 9f7b98147ccd
General Reverse Engineering is a skill published in the GitHub repository buzzer-re/Rikugan (673 stars, last pushed 2mo ago), licensed MIT. It adds 16 tokens to every session and 653 once invoked, about $0.0001 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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