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 CodeInfinity1/Stram --skill python-debugginggit clone --depth 1 https://github.com/CodeInfinity1/StramWrote 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/codeinfinity1/stram/python-debugging)<a href="https://agentmods.dev/skills/codeinfinity1/stram/python-debugging"><img src="https://agentmods.dev/badge/skills/codeinfinity1/stram/python-debugging/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/codeinfinity1/stram/python-debugging"><img src="https://agentmods.dev/badge/skills/codeinfinity1/stram/python-debugging.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 | $0.00030 | $0.00427 |
| Opus 5.5 | $0.00012 | $0.00171 |
| Sonnet 5.5 | $0.00006 | $0.00085 |
| Haiku 4.5 | $0.00003 | $0.00043 |
Grade A, and why
python-debugging 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.
What it actually says
Python Debugging
Purpose
Diagnose Python failures through tests, stack traces, imports, environment, and small safe experiments. Use python_interpreter for bounded analysis when useful.
When To Use
Use for pytest failures, import errors, scripts, API bugs, data processing issues, virtualenv confusion, and Python runtime behavior questions.
Inputs And Evidence
- Traceback, failing command, source files, tests, pyproject/requirements, interpreter path, and environment.
- Interpreter run manifests and artifacts when used.
Tool Map
read_filesearch_workspacerun_shell_commandpython_interpreterpython_interpreter_runspython_interpreter_runsystematic-debugging
Workflow
- Identify interpreter and environment.
- Reproduce the failure or inspect current traceback.
- Trace through source, imports, and tests.
- Use
python_interpreterfor isolated analysis when approved. - Patch the root cause.
- Run targeted tests, py_compile, or broader suites as risk requires.
Native Implementation Boundaries
- Use Stram Python interpreter and native shell tools.
- Do not import external reference debugpy scripts as implementation.
- Do not execute untrusted Python without sandbox/profile awareness.
Safety And Approval
python_interpreteris approval-gated.- Keep network/subprocess/import permissions minimal.
- Do not leak env secrets in trace/log summaries.
Verification
- Show exact command outcomes.
- Inspect interpreter artifacts only through native artifact tools.
- State if local Python shim or virtualenv issues block a check.
Failure Modes
- Running tests with the wrong interpreter.
- Fixing import paths in a way that masks packaging problems.
- Over-broad exception swallowing.
References
- Shortlist item:
python-debugging. - Native tools: Stram Python interpreter and shell tools.
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 · 68 lines · 30 tokens per session scan A eb7f08758707
python-debugging is a skill published in the GitHub repository CodeInfinity1/Stram (11 stars, last pushed 2mo ago), licensed MIT. It adds 30 tokens to every session and 427 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 2026-09-03.
Other skills, from other repositories
omh-live-incident-response
This is an OMH live-incident-response workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
debugging
Use this skill when something is broken and the cause is unknown — a failing test, a crash, an error message, wrong output, a regression, a hang, or behaviour that differs between environments — and it has to be found and fixed at the root. Also use it to build the failing proof for a suspected defect before fixing…
thinking-five-whys-plus
When a fault is localized and the proximate cause is known but the systemic root is not, chain evidence-linked whys with a counterfactual stop and a countermeasure.
thinking-map-territory
When a claim, doc, test, metric, or assumption conflicts with observed behavior, stop theorizing from the map and verify the live code or data; let territory overrule.
debug-systematic
Systematic 4-phase debugging methodology for complex, intermittent, or mysterious issues. Use when investigating bugs, race conditions, or unexplained failures.
postmortem
Deliver a structured post-mortem after incidents, mistakes, or stuck sessions. Use when the user requests a structured post-mortem after incidents, mistakes, or stuck sessions.