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 FoloToy/ai-passport --skill passport-debuggit clone --depth 1 https://github.com/FoloToy/ai-passportWrote 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/folotoy/ai-passport/passport-debug)<a href="https://agentmods.dev/skills/folotoy/ai-passport/passport-debug"><img src="https://agentmods.dev/badge/skills/folotoy/ai-passport/passport-debug/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/folotoy/ai-passport/passport-debug"><img src="https://agentmods.dev/badge/skills/folotoy/ai-passport/passport-debug.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 | $0.00049 | $0.00783 |
| Opus 5.5 | $0.00020 | $0.00313 |
| Sonnet 5.5 | $0.00010 | $0.00157 |
| Haiku 4.5 | $0.00005 | $0.00078 |
Grade A, and why
passport-debug 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnose Firmware Failures
Locate the affected checkout and read AGENTS.md. Start with
git status --short --branch. Determine whether the user wants an explanation
or a fix: diagnosis does not authorize edits, rebuilding, flashing, or pushing.
The supplied log may come from another firmware; the current checkout is not
automatically its source.
Establish the evidence
- Record the symptom, reproducible user action, first relevant error, reset/ exception type, task, firmware version, and ELF SHA shown in the log. Request only missing information that would change the conclusion; do not request credentials, raw device QR content, or a whole private environment dump.
- Distinguish observation, likely cause, and unverified hypothesis. A stack pointer outside the reported task bounds is direct evidence of a stack fault; the exact allocating/calling function still requires matching symbols.
- For the current application's code, inspect the relevant headers and
implementation. Use
docs/hardware-design/AI_HARDWARE_DEVELOPMENT_GUIDE.mdfor the matching symptom, not a generic ESP32 board's pins or assumptions.
Match symbols before decoding addresses
For a saved gate build, verify its bundle with
python3 tools/archive_firmware.py verify <archive-directory>. Compare the
panic/boot ELF identity with app_elf_sha256 in its manifest. A truncated log
hash must match a unique candidate; ask for the complete identity if ambiguous.
For other builds, obtain the ELF that produced the flashed image and evidence
of its identity. A similar Git version or a later rebuild is not a substitute.
Only after matching, use the activated toolchain's
riscv32-esp-elf-addr2line -pfiaC -e <matching.elf> <addresses> for the panic PC,
return address, and credible trace frames. Raw stack words are not all return
addresses. If symbols are unavailable, explain what the log itself establishes
and request the matching build artifacts; do not invent file/line locations or
rebuild the current source and call it the old firmware.
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.
- 1mo ago First seen · 67 lines · 49 tokens per session scan A 03101fba51a6
passport-debug is a skill published in the GitHub repository FoloToy/ai-passport (646 stars, last pushed 2d ago), licensed MIT. It adds 49 tokens to every session and 783 once invoked, about $0.0002 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 2026-09-17.
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