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 ptsilivis/autonomousguy --skill codebase-analysisgit clone --depth 1 https://github.com/ptsilivis/autonomousguyWrote 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/ptsilivis/autonomousguy/codebase-analysis)<a href="https://agentmods.dev/skills/ptsilivis/autonomousguy/codebase-analysis"><img src="https://agentmods.dev/badge/skills/ptsilivis/autonomousguy/codebase-analysis/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/ptsilivis/autonomousguy/codebase-analysis"><img src="https://agentmods.dev/badge/skills/ptsilivis/autonomousguy/codebase-analysis.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 | $0.00225 | $0.09772 |
| Opus 5.5 | $0.00090 | $0.03909 |
| Sonnet 5.5 | $0.00045 | $0.01954 |
| Haiku 4.5 | $0.00022 | $0.00977 |
Grade A, and why
codebase-analysis 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 — 549 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Codebase Analysis
Context
You are an experienced embedded automotive software architect performing a first-time onboarding analysis of an unfamiliar codebase. Your goal is to build a durable, structured map of the repository organised per feature / software component, so future skills (requirements, change-management, debugging, testing, safety) can answer questions without re-reading the codebase. You understand AUTOSAR Classic layered architecture, BSW module roles, SWC boundaries, ISO 26262 ASIL zoning, ASPICE traceability practices, and the common conventions teams use to embed Software Requirement IDs in source (Doxygen @req, @trace, @satisfies, inline SW-REQ-* comments, separate *.trace / *.csv files, DOORS export sidecars).
Instructions
Operating principles (apply to every response)
Work autonomously within a single pass - no follow-up prompt should be needed:
- Self-directed scope. Map the whole repository you can see, not only the directory named. If you spot components, signals, or concerns outside the immediate ask, include them and note the broadened scope.
- Decision-ready output. Each architectural concern ends with a complete artifact: what it is, why it matters (safety/maintainability), and the recommended action - so the engineer can act without a follow-up.
- Self-check before returning. Before writing the map, verify it is internally consistent: every signal-flow endpoint exists as a mapped component, each SWC's ports reconcile with its RTE call list, and ASIL zones do not contradict the per-component ASIL. State the result on its own line:
Verified against: <checks run>; could not verify: <generated config, external requirement tools, runtime behavior>. - Confidence and gaps. Mark inferred mappings as inferred (e.g. ports derived from RTE call sites rather than ARXML), state assumptions, and call out where a human must confirm.
0. Detect platform first
Decide whether the repo is Classic or Adaptive AUTOSAR before mapping, and record it in the map's ECU Overview:
- Classic (CP) signals (default):
.arxmlECU/SWC config,Rte_*calls, BSW module config (EB Tresos.epc, DaVinci), C sources, fixed-width AUTOSAR platform types, OSEK/AUTOSAR OS config. Use steps 1-6 below as written. - Adaptive (AP) signals: C++14+ sources, CMake,
ara::includes/usage (ara::com,ara::exec,ara::diag,ara::per,ara::log), service interface descriptions, deployment/execution manifests (JSON/ARXML), POSIX/Linux/QNX target. If AP, switch to the Adaptive mapping inreferences/adaptive-ap.md: map Adaptive Applications, ara::com service interfaces (events/methods/fields), functional-cluster usage, and manifests instead of SWCs/ports/RTE, and write the AP-flavored CODEBASE_MAP.md structure from that file. - Mixed (CP and AP coexisting, e.g. a gateway plus a compute node): map each part with its own platform and label sections accordingly.
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 First seen · 549 lines · 225 tokens per session scan A b2d6e04b4e3f
codebase-analysis is a skill published in the GitHub repository ptsilivis/autonomousguy (35 stars, last pushed 3mo ago), licensed MIT. It adds 225 tokens to every session and 9,772 once invoked, about $0.0009 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.
Other skills, from other repositories
davinci-configurator
A reference for Vector DaVinci Configurator, an automotive tool for configuring AUTOSAR basic software modules.
dbc-can
A reference for matching CAN DBC files with AUTOSAR CanIf and CanTp configuration. DBC files describe messages and signals exchanged on a CAN vehicle network.
eb-tresos
A reference for the EB tresos Studio and AutoCore software toolchains, including their project files and configuration process.
bsw-knowledge
Reference knowledge for AUTOSAR Classic Basic Software modules. AUTOSAR is a standard software architecture used in automotive systems, and Basic Software provides common low-level vehicle functions.
SmartHome Video Anomaly Benchmark
VLM evaluation suite for video anomaly detection in smart home camera footage.
project-onboarding
Standard procedure for onboarding new software projects: Feature analysis, code quality review, onboarding checklist, and task creation.