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/notactuallytreyanastasio/deciduous/serve-uigit clone --depth 1 https://github.com/notactuallytreyanastasio/deciduousWrote 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/commands/notactuallytreyanastasio/deciduous/serve-ui)<a href="https://agentmods.dev/commands/notactuallytreyanastasio/deciduous/serve-ui"><img src="https://agentmods.dev/badge/commands/notactuallytreyanastasio/deciduous/serve-ui.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 | $0.00000 | $0.00280 |
| Opus 5 | $0.00000 | $0.00140 |
| Sonnet 5 | $0.00000 | $0.00056 |
| Haiku 4.5 | $0.00000 | $0.00028 |
Grade A, and why
serve-ui 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 5d 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
Start Decision Graph Viewer
Launch the deciduous web server for viewing and navigating the decision graph.
Instructions
-
Start the server:
deciduous serve --port 3000 -
Inform the user:
- The server is running at http://localhost:3000
- The graph auto-refreshes every 30 seconds
- They can browse decisions, chains, and timeline views
- Changes made via CLI will appear automatically
-
The server will run in the foreground. Remind user to stop it when done (Ctrl+C).
UI Features
- Chains View: See decision chains grouped by goals
- Timeline View: Chronological view of all decisions
- Graph View: Interactive force-directed graph
- DAG View: Directed acyclic graph visualization
- Detail Panel: Click any node to see full details including:
- Node metadata (confidence, commit, prompt, files)
- Connected nodes (incoming/outgoing edges)
- Timestamps and status
Alternative: Static Hosting
For GitHub Pages or other static hosting:
deciduous sync # Exports to docs/graph-data.json
Then push to GitHub - the graph is viewable at your GitHub Pages URL.
$ARGUMENTS
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.
- 5d ago First seen · 40 lines · 0 tokens per session scan A 23cd8c9e2a43
serve-ui is a command published in the GitHub repository notactuallytreyanastasio/deciduous (160 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 280 tokens. 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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.