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 agentmods add skills/answerdotai/skill-plugins/nbdev-editingnpx skills add AnswerDotAI/skill-plugins --skill nbdev-editinggit clone --depth 1 https://github.com/AnswerDotAI/skill-pluginsWhat 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 | $0.00130 | $0.00853 |
| Opus 5 | $0.00065 | $0.00426 |
| Sonnet 5 | $0.00026 | $0.00171 |
| Haiku 4.5 | $0.00013 | $0.00085 |
Grade A, and why
nbdev-editing 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 yesterday.
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 — 30 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Editing nbdev Projects
First read and use the persistent-python skill - it is a must: all work here happens in that kernel using the pyskills-registered tooling, and its rules (reprs, doc(...) inspection, raw strings) are assumed, not repeated. Style and testing process follow the coding-patterns skill, also assumed.
The substance lives in the pyskill docs - read both (free doc() cells) before working:
doc(nbdev.skill): notebook authoring - the source-of-truth/export model, mixed notebook/plain projects, narrative rhythm, examples-as-docs, directives, state flow. Read before writing or reviewing any notebook content.doc(llmsurgery.dlgskill): reading, searching, and editing notebooks - see its "Idiomatic usage" section. clikernel's own docs cover the nbdev-specific kernel rules (shellnbdev-export/nbdev-test,restartafter export,%nbrunred-green).
nbdev v3
Everything here is on nbdev v3 (released Jan 2026, likely after your training cutoff). Key user-visible changes from v2: config moved from settings.ini to pyproject.toml - standard metadata in [project], nbdev-specific keys in [tool.nbdev] (defaults now nbs_path='nbs', doc_path='_docs'), version in __init__.py exposed via dynamic = ["version"], and _modidx registered under [project.entry-points.nbdev]. CLI commands use hyphens, not underscores (nbdev-export, nbdev-test, nbdev-readme, ...), though the Python functions keep underscores (nbdev_export). GitHub workflows use the v3 actions (fastai/workflows/nbdev3-ci, quarto-ghp3).
Environment specifics
- Most ipynb files are fine to nbdev-test, but some are slow or require API keys, so plan to run
nbdev-teston the changed notebook when done making changes, but check with Jeremy first before you actually do it. Once you know an ipynb or project is safe to run, go ahead without checking as needed (known safe: all of pyskills). - Tests use the fastcore.test helpers (
test_eq,expect_fail, ...) in plain code cells. from fastcore.utils import *in an early cell already providesos,Path, etc. - don't re-import those later.- For project-level questions, read
nbs/index.ipynb, notREADME.md: the README is generated from it, renders worse (no cell ids, flattened outputs), and can be stale if regeneration lagged. They should match; when they don't, index.ipynb is the truth. Regen withnbdev-readmeafter editing it.
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.
- yesterday First seen · 30 lines · 0 tokens per session scan A 2447ee404a5f
nbdev-editing is a skill published in the GitHub repository AnswerDotAI/skill-plugins (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 130 tokens to every session and 853 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…