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 pproenca/dot-skills --skill python-pep-authorgit clone --depth 1 https://github.com/pproenca/dot-skillsWrote 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/pproenca/dot-skills/python-pep-author)<a href="https://agentmods.dev/skills/pproenca/dot-skills/python-pep-author"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/python-pep-author/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/pproenca/dot-skills/python-pep-author"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/python-pep-author.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 | $0.00185 | $0.01985 |
| Opus 5 | $0.00093 | $0.00992 |
| Sonnet 5 | $0.00037 | $0.00397 |
| Haiku 4.5 | $0.00018 | $0.00198 |
Grade A, and why
python-pep-author 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 6d 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write a Python Enhancement Proposal (PEP)
A PEP is the design document the Python community uses to propose a new language feature, a stdlib change, an interoperability standard, or a process/informational guideline. This skill takes an author from a rough idea to a correctly-formatted, process-compliant draft ready for submission to the python/peps repository.
The hard parts of a PEP are not the prose — they are getting the type right, the header preamble valid, the sections complete to the acceptance bar, and following the process (vetting, sponsorship, review). This skill bundles two scripts for the deterministic parts and reference docs for the judgement calls.
When to Apply
- The user asks to write / draft / structure a PEP or a "Python Enhancement Proposal".
- The user wants to propose a Python language or standard-library feature and needs it written up formally.
- The user has an idea they've been discussing on the Python Discourse and wants to turn it into a PEP draft.
- The user needs the PEP template, header fields, or section structure explained or generated.
- The user is revising an existing PEP (changing status, adding a Resolution, addressing review feedback).
Do not use this skill for internal company RFCs / design docs (use dev-rfc) — a
PEP is specifically a proposal to the upstream CPython / Python community governed by
PEP 1.
Prerequisites
- Bash + coreutils (
awk,sed,grep,date) for the two scripts — present by default on macOS/Linux. - A clone of, or a fork of, github.com/python/peps only when you're ready to submit (Step 6). Drafting needs no repo.
- No Python runtime is required to draft or lint; the reference implementation (if any) is the author's separate codebase.
Workflow Overview
1. Vet the idea ────► 2. Choose the type ────► 3. Scaffold the file
(is it PEP-able?) (Standards/Info/Process) (scripts/new-pep.sh)
│
6. Submit ◄──── 5. Self-check ◄──── 4. Draft each section
(sponsor, PR) (scripts/check-pep.sh) (to the acceptance bar)
│
▼
7. Review & resolution ──► update Status + Resolution header
What ships with it
10 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.
- assets/templates/pep-template.rst 3.3 KB
- gotchas.md 2.2 KB
- metadata.json 635 B
- references/header-fields.md 3.7 KB
- references/pep-types.md 3.0 KB
- references/sections.md 4.4 KB
- references/status-lifecycle.md 2.7 KB
- references/workflow.md 6.5 KB
- scripts/check-pep.sh 4.5 KB runs code
- scripts/new-pep.sh 5.4 KB runs code
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.
- 6d ago First seen · 153 lines · 185 tokens per session scan A 3bec86b8ef64
python-pep-author is a skill published in the GitHub repository pproenca/dot-skills (205 stars, last pushed 24d ago), licensed MIT. It adds 185 tokens to every session and 1,985 once invoked, about $0.0009 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 2026-09-03.
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