Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
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 NousResearch/hermes-agent --skill python-debugpygit clone --depth 1 https://github.com/NousResearch/hermes-agentWrote 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/nousresearch/hermes-agent/python-debugpy)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/python-debugpy"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/python-debugpy/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/nousresearch/hermes-agent/python-debugpy"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/python-debugpy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 193 Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
- medium Privilege Escalation · line 193 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
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 | $0.00017 | $0.03617 |
| Opus 5.5 | $0.00007 | $0.01447 |
| Sonnet 5.5 | $0.00003 | $0.00723 |
| Haiku 4.5 | $0.00002 | $0.00362 |
Grade B, and why
python-debugpy scanned grade B with 1 finding 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
echo 0 | sudo tee /proc/sys/kernel/yama/ptrace_scope Copies of this mod
8 near-identical copies found in the catalogue:
- python-debugpy — 100% identical, 0 lines differ
- python-debugpy — 100% identical, 0 lines differ
- python-debugpy — 97% identical, 48 lines differ
- python-debugpy — 95% identical, 22 lines differ
- python-debugpy — 95% identical, 25 lines differ
- python-debugpy — 95% identical, 23 lines differ
- python-debugpy — 95% identical, 22 lines differ
- python-debugpy — 95% identical, 44 lines differ
How it starts
The opening of the file, as written. The whole thing — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Debugger (pdb + debugpy)
Overview
Three tools, picked by situation:
| Tool | When |
|---|---|
breakpoint() + pdb |
Local, interactive, simplest. Add breakpoint() in the source, run normally, get a REPL at that line. |
python -m pdb |
Launch an existing script under pdb with no source edits. Useful for quick poking. |
debugpy |
Remote / headless / "attach to already-running process." Talks DAP, scriptable from terminal, works for long-lived processes (gateway, daemon, PTY children). |
Start with breakpoint(). It's the cheapest thing that works.
When to Use
- A test fails and the traceback doesn't reveal why a value is wrong
- You need to step through a function and watch a collection mutate
- A long-running process (hermes gateway, tui_gateway) misbehaves and you can't restart it
- Post-mortem: an exception fired in prod-ish code and you want to inspect locals at the crash site
- A subprocess / child (Python
_SlashWorker, PTY bridge worker) is the actual bug site
Don't use for: things print() / logging.debug solve in under a minute, or things pytest -vv --tb=long --showlocals already reveals.
pdb Quick Reference
Inside any pdb prompt ((Pdb)):
| Command | Action |
|---|---|
h / h cmd |
help |
n |
next line (step over) |
s |
step into |
r |
return from current function |
c |
continue |
unt N |
continue until line N |
j N |
jump to line N (same function only) |
l / ll |
list source around current line / full function |
w |
where (stack trace) |
u / d |
move up / down in the stack |
a |
print args of the current function |
p expr / pp expr |
print / pretty-print expression |
display expr |
auto-print expr on every stop |
b file:line |
set breakpoint |
b func |
break on function entry |
b file:line, cond |
conditional breakpoint |
cl N |
clear breakpoint N |
tbreak file:line |
one-shot breakpoint |
!stmt |
execute arbitrary Python (assignments included) |
interact |
drop into full Python REPL in current scope (Ctrl+D to exit) |
q |
quit |
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 · 374 lines · 17 tokens per session scan B 24258d93eb08
python-debugpy is a skill published in the GitHub repository NousResearch/hermes-agent (255,586 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 3,617 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
module-regression
Govern cross-module regression in large repositories with a REGRESSION.md ledger that records each module's downstream consumers and executable acceptance command. After a change, run the changed module and every affected downstream command, and use observed exit codes as regression evidence under the project's…
agent-introspection-debugging
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.
change-impact
Perform evidence-backed impact analysis before changing code, data, interfaces, or governance documents, then compare the planned impact with the actual diff after implementation. Covers call paths, data and schema, API contracts, tests, documentation, ADRs, deployment, migration, and rollback. Use for cross-module or…
caveman-explore
Read-only repository explorer for cold-start orientation, broad cross-file localization, or when a direct search failed. Skip it when the exact file or symbol is already named. Returns path:line citations only; its reads stay out of main context.
investigate-first
Diagnose ambiguous failures before editing. Use for unknown causes, intermittent behavior, performance regressions, or investigations needing evidence-ranked hypotheses.
surgical-patch
Fix bugs and small behavior changes at the narrowest responsible layer. Use when regression proof, preserved surrounding behavior, and task-relevant tests matter.