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 softspark/ai-toolkit --skill explaingit clone --depth 1 https://github.com/softspark/ai-toolkitWrote 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/softspark/ai-toolkit/explain)<a href="https://agentmods.dev/skills/softspark/ai-toolkit/explain"><img src="https://agentmods.dev/badge/skills/softspark/ai-toolkit/explain/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/softspark/ai-toolkit/explain"><img src="https://agentmods.dev/badge/skills/softspark/ai-toolkit/explain.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 | $0.00034 | $0.00818 |
| Opus 5.5 | $0.00014 | $0.00327 |
| Sonnet 5.5 | $0.00007 | $0.00164 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
explain 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.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explain
$ARGUMENTS
Generates visual architecture explanations.
Output Format
1. High-Level Role
"This module handles [Responsibility]. It interacts with [Dependencies]."
2. Dependency Graph (Mermaid)
Generate a graph showing imports/exports.
graph TD
A[AuthService] -->|uses| B[UserRepo]
A -->|validates| C[Schema]
D[Controller] -->|calls| A
3. Key Flows (Sequence)
If logical flows are detected:
sequenceDiagram
User->>Controller: Login
Controller->>Service: Validate
Service->>DB: Check Creds
DB-->>Service: Result
Service-->>Controller: Token
Protocol
- Scan: Read file contents to identify classes and functions.
- Link: Identify imports to find collaborators.
- Visualize: Generate standard Mermaid syntax.
Automated Dependency Graph
Run the bundled script to extract imports and generate a Mermaid diagram:
python3 ${CLAUDE_SKILL_DIR}/scripts/dependency-graph.py src/auth.py
Rules
- MUST start from what the user already knows — if it is unclear, ask one question before explaining
- MUST ground the explanation in the actual code (file:line references), not in generic framework theory
- NEVER use an analogy when a direct definition is clearer — analogies add a translation step for the reader
- NEVER produce a diagram that the text does not already justify — diagrams illustrate, they do not replace the explanation
- CRITICAL: when the code base is large, scope the explanation to one entry point plus its immediate collaborators. Explaining "the whole system" in one pass fails for any non-trivial project.
- MANDATORY: if the user asks for a short answer, give a one-paragraph summary without diagrams — not every request needs a Mermaid graph
Gotchas
- Mermaid renders differently across GitHub, VS Code preview, and static generators. Features added post-2023 (e.g., class diagram relations,
accTitle) may render as raw text on older renderers. Stick to the basic subset unless you know the target. dependency-graph.pyparses imports statically; dynamic imports (__import__,importlib.import_module, JavaScriptawait import()) are invisible. Note explicitly when the generated graph is likely incomplete.- Sequence diagrams have no notion of async vs sync. Two parallel calls render as sequential; distinguish with a
parblock or a note. - Architectural explanations that name "the service layer" or "the controller" leak framework jargon. If the project does not use those terms, use the project's own names — otherwise the reader is translating twice.
- Long Mermaid graphs wrap awkwardly on narrow screens. For >20 nodes, split into a high-level graph and drill-down graphs rather than one giant diagram.
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.
- 1mo ago First seen · 80 lines · 34 tokens per session scan A 39e05bfaa869
explain is a skill published in the GitHub repository softspark/ai-toolkit (180 stars, last pushed 8d ago), licensed Apache-2.0. It adds 34 tokens to every session and 818 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.
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