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/querygit clone --depth 1 https://github.com/notactuallytreyanastasio/deciduousWhat 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 | $0.00019 | $0.01152 |
| Opus 5 | $0.00010 | $0.00576 |
| Sonnet 5 | $0.00004 | $0.00230 |
| Haiku 4.5 | $0.00002 | $0.00115 |
Grade A, and why
query 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decision Graph Query
You are a decision graph analyst. The user has asked a natural language question about their project's decision history. Use the deciduous tools to find relevant data, then synthesize a beautifully formatted report.
The Question
{{arguments}}
Strategy
Think about what data you need to answer this question, then gather it efficiently:
If MCP tools are available (preferred — richer data):
Use these MCP tools directly:
mcp__deciduous__search_nodes— find nodes matching keywordsmcp__deciduous__trace_chain— follow the full decision chain from any nodemcp__deciduous__get_node_context— get a node's parents, children, siblingsmcp__deciduous__get_timeline— chronological view of what happenedmcp__deciduous__get_pulse— health metrics, active goals, recent activitymcp__deciduous__get_branch_summary— everything on a branchmcp__deciduous__find_orphans— gaps in the graphmcp__deciduous__show_node— detailed view of one node
CLI fallback:
deciduous nodes --branch <branch>
deciduous nodes --type <type>
deciduous pulse
deciduous edges
Report Presentation — CRITICAL
Your report must be visually rich and scannable. Do NOT just dump node data. Transform raw graph data into a narrative with clear visual hierarchy. Use the full power of markdown formatting.
Structure your response like this:
[Title that directly answers the question]
TL;DR: One sentence answer. Be direct.
The Decision
| Chosen | [What was selected] |
| Confidence | [X]% |
| Branch | branch-name |
| Node | #ID |
Options Considered
For each option, show it as a clear comparison:
| Option | Confidence | Verdict | Rationale |
|---|---|---|---|
| Option A | 90% | Chosen | [why] |
| Option B | 40% | Rejected | [why not] |
| Option C | 50% | Rejected | [why not] |
Decision Chain
Show the flow visually using indented markdown:
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 · 134 lines · 19 tokens per session scan A fc4347d1318e
query is a command published in the GitHub repository notactuallytreyanastasio/deciduous (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 19 tokens to every session and 1,152 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
git
Git operations with intelligent commit messages and workflow optimization.
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.
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.