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 commands/nicobailon/visual-explainer/generate-visual-plangit clone --depth 1 https://github.com/nicobailon/visual-explainerWhat 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 | $0.00010 | $0.00266 |
| Opus 5 | $0.00005 | $0.00133 |
| Sonnet 5 | $0.00002 | $0.00053 |
| Haiku 4.5 | $0.00001 | $0.00027 |
Grade A, and why
generate-visual-plan 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.
What it actually says
Load the visual-explainer skill and generate a self-contained HTML implementation plan for: $@
Research first
Read relevant repo files before planning. Identify entry points, existing patterns, affected modules, public APIs, tests, config/schema/data model, similar features, and constraints from README/CHANGELOG/docs.
Required page sections
- Goal and scope: what will change and what is intentionally out.
- Current state: short diagram/table of existing architecture.
- Proposed design: architecture/data/control flow, preferably Mermaid or hybrid cards.
- Implementation sequence: ordered phases with dependencies.
- File map: files to create/edit/delete and why.
- Interface/contracts: types, APIs, schemas, CLI flags, config, events.
- Risk and decision matrix: correctness, tests, migration, release, UX, security/privacy, performance.
- Test plan: unit/integration/e2e/edge cases mapped to files.
- Acceptance checklist: observable done criteria.
Use hierarchy: overview and architecture dominate; detailed file/test/reference sections stay compact or collapsible. Follow the skill’s Mermaid, table, overflow, and delivery rules.
Write to ~/.agent/diagrams/ and open in browser.
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 · 27 lines · 10 tokens per session scan A c44be699b455
generate-visual-plan is a command published in the GitHub repository nicobailon/visual-explainer (9,621 stars, last pushed 4d ago), licensed MIT. It adds 10 tokens to every session and 266 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.