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/nexus-substrate/nexus-agents/docs-chartnpx skills add nexus-substrate/nexus-agents --skill docs-chartgit clone --depth 1 https://github.com/nexus-substrate/nexus-agentsWrote 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/nexus-substrate/nexus-agents/docs-chart)<a href="https://agentmods.dev/skills/nexus-substrate/nexus-agents/docs-chart"><img src="https://agentmods.dev/badge/skills/nexus-substrate/nexus-agents/docs-chart.svg" alt="Measured on agentmods" 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 | $0.00099 | $0.01447 |
| Opus 5 | $0.00049 | $0.00724 |
| Sonnet 5 | $0.00020 | $0.00289 |
| Haiku 4.5 | $0.00010 | $0.00145 |
Grade C, and why
docs-chart scanned grade C 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 3d 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- CANONICAL SOURCES: - .rules/docs-rubric.md (Source/Evidence dimensions) - skills/docs-mermaid (precise diagrams — sequence/state/etc.) - skills/docs-image (illustrative AI-gen) - existing CLI: `nexus-agents usage` a How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inline Data Visualization Charts Skill
When to apply
Use inline SVG charts when nexus-agents docs reference quantitative data:
| Data source | Best chart type |
|---|---|
| OutcomeStore success-rate-by-CLI | Horizontal bar |
Per-call cost from usage-log.jsonl |
Donut (per model) |
| Fitness audit history (target ≥ 90/100) | Line chart |
| CLI success rate over time | Line / area |
| Voter approval distribution | Lollipop |
| Cost-per-success comparison | Grouped bar |
| Multi-dimensional model scoring (reasoning / speed / cost / quality) | Radar |
Mermaid handles sequence / state / flow diagrams (see docs-mermaid),
but renders quantitative charts ugly and inflexibly. SVG is the right
tool for "compare these numbers." Each rendered chart is dark-mode-
compatible (uses currentColor for text + transparent backgrounds) and
includes a role="img" + aria-label for accessibility.
Step 1 — Identify the chart type
Look at the data pattern:
| Pattern | Chart type |
|---|---|
| Before/after comparison (claude vs codex success rates) | Grouped bar |
| Ranked factors / correlations (per-CLI categories) | Lollipop |
| Parts of whole / market share (cost split per model) | Donut |
| Trend over time (fitness score by week) | Line |
| Percentage improvement (single dimension) | Horizontal bar |
| Distribution / range (latency p50/p99) | Area |
| Multi-dimensional scoring | Radar |
What ships with it
1 file 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.
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.
- 3d ago First seen · 146 lines · 99 tokens per session scan C 2f9a6d95928c
docs-chart is a skill published in the GitHub repository nexus-substrate/nexus-agents (18 stars, last pushed today), licensed MIT. It adds 99 tokens to every session and 1,447 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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…