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/stellarshenson/claude-code-plugins/footnotesnpx skills add stellarshenson/claude-code-plugins --skill footnotesgit clone --depth 1 https://github.com/stellarshenson/claude-code-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 | $0.00054 | $0.00842 |
| Opus 5 | $0.00027 | $0.00421 |
| Sonnet 5 | $0.00011 | $0.00168 |
| Haiku 4.5 | $0.00005 | $0.00084 |
Grade A, and why
footnotes 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 2d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Footnotes in Markdown
Standard [^1] footnotes unsupported in Jupyter. Use HTML anchor pattern. Proven in JupyterLab (incl. id sanitizer), GitHub, standard markdown.
How it works in JupyterLab
- Markdown writes
<span id="D005"> - JupyterLab sanitizer renames
idtodata-jupyter-id="D005"(not deleted) - User clicks blue superscript link
[<sup>D005</sup>](#D005) - JupyterLab click handler matches
#D005againstdata-jupyter-id="D005" - Calls
scrollIntoView()on target
Pattern
Inline reference (clickable superscript):
- dowód: [<sup>D005</sup>](#D005) dowody/03 komunikacja z matką/2023-09-06 pismo.pdf
Renders: clickable blue superscript D005 + file path.
Target anchor (references section):
- <span id="D005">D005 `dowody/03 komunikacja z matką/2023-09-06 pismo.pdf`</span>
Renders: bullet with D005 label + monospace path.
Requirements
- Target MUST use
<span id="...">. Not<div>, not heading - Inline link MUST use
(#DXXX)hash. Not relative path - No
<br>between entries - bullets handle spacing <sup>inside link optional. NOT inside target span- IDs unique across document
Numbering Schemes
| Context | Pattern | Example |
|---|---|---|
| Evidence/documents | D001, D002 |
[<sup>D005</sup>](#D005) |
| General footnotes | fn1, fn2 |
[<sup>1</sup>](#fn1) |
| Paper citations | ref1, ref2 |
[<sup>ref3</sup>](#ref3) |
| Named references | fn_dataset, fn_paper |
[<sup>*</sup>](#fn_dataset) |
Full Example
## Timeline
- **2023-01-15 - Author submits proposal to committee**
- source: email
- evidence: [<sup>D001</sup>](#D001) documents/2023-01-15 proposal submission.pdf
- category: Submissions
- **2023-02-20 - Committee responds with revision request**
- source: letter
- evidence: [<sup>D002</sup>](#D002) documents/2023-02-20 revision request.pdf
- category: Responses
---
## References
- <span id="D001">D001 `documents/2023-01-15 proposal submission.pdf`</span>
- <span id="D002">D002 `documents/2023-02-20 revision request.pdf`</span>
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.
- 2d ago First seen · 87 lines · 54 tokens per session scan A b5c78d9aff31
footnotes is a skill published in the GitHub repository stellarshenson/claude-code-plugins (3 stars, last pushed 3d ago), licensed MIT. It adds 54 tokens to every session and 842 once invoked, about $0.0003 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.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
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…