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/thapaliyabikendra/ai-artifacts/adrgit clone --depth 1 https://github.com/thapaliyabikendra/ai-artifactsWrote 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/thapaliyabikendra/ai-artifacts/adr)<a href="https://agentmods.dev/commands/thapaliyabikendra/ai-artifacts/adr"><img src="https://agentmods.dev/badge/commands/thapaliyabikendra/ai-artifacts/adr.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 5.1 | $0.00010 | $0.01196 |
| Opus 5 | $0.00005 | $0.00598 |
| Sonnet 5 | $0.00002 | $0.00239 |
| Haiku 4.5 | $0.00001 | $0.00120 |
Grade A, and why
adr 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 6d 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Architecture Decision Record
Title: $ARGUMENTS.title Number: $ARGUMENTS.number
Instructions
Step 1: Determine ADR Number
If number not provided:
- Search
docs/architecture/decisions/for existing ADRs - Find highest number:
ADR-NNNN-*.md - Increment by 1
Step 2: Gather Context
Before writing, consider:
- What problem are we solving?
- What options were considered?
- What constraints exist?
- Who are the stakeholders?
- What are the trade-offs?
Step 3: Generate ADR
# ADR-[NNNN]: [Title]
**Date**: [YYYY-MM-DD]
**Status**: Proposed | Accepted | Deprecated | Superseded by ADR-XXXX
**Deciders**: [List of people involved]
**Technical Story**: [Link to issue/story if applicable]
## Context
[Describe the context and problem statement. What is the issue that we're seeing that is motivating this decision?]
### Current State
[Describe how things work today, if applicable]
### Problem Statement
[Clear statement of the problem to be solved]
### Constraints
- [Constraint 1: e.g., Must work with existing database]
- [Constraint 2: e.g., Budget limitation]
- [Constraint 3: e.g., Timeline pressure]
## Decision Drivers
- [Driver 1: e.g., Performance requirements]
- [Driver 2: e.g., Maintainability]
- [Driver 3: e.g., Team expertise]
- [Driver 4: e.g., Cost]
## Considered Options
### Option 1: [Name]
[Brief description]
**Pros:**
- [Pro 1]
- [Pro 2]
**Cons:**
- [Con 1]
- [Con 2]
**Effort**: Low | Medium | High
**Risk**: Low | Medium | High
### Option 2: [Name]
[Brief description]
**Pros:**
- [Pro 1]
- [Pro 2]
**Cons:**
- [Con 1]
- [Con 2]
**Effort**: Low | Medium | High
**Risk**: Low | Medium | High
### Option 3: [Name]
[Brief description]
**Pros:**
- [Pro 1]
- [Pro 2]
**Cons:**
- [Con 1]
- [Con 2]
**Effort**: Low | Medium | High
**Risk**: Low | Medium | High
## Decision
**Chosen Option**: [Option N] - [Name]
[Justify why this option was selected over others]
### Rationale
[Detailed explanation of why this decision was made, referencing the decision drivers]
## Consequences
### Positive
- [Positive consequence 1]
- [Positive consequence 2]
### Negative
- [Negative consequence 1 and how we'll mitigate it]
- [Negative consequence 2 and how we'll mitigate it]
### Neutral
- [Neutral observation]
## Implementation
### Action Items
- [ ] [Action 1]
- [ ] [Action 2]
- [ ] [Action 3]
### Timeline
| Phase | Task | Target Date |
|-------|------|-------------|
| 1 | [Task] | [Date] |
| 2 | [Task] | [Date] |
### Success Metrics
| Metric | Current | Target |
|--------|---------|--------|
| [Metric 1] | [Value] | [Value] |
| [Metric 2] | [Value] | [Value] |
## Related Decisions
- [ADR-XXXX](./ADR-XXXX-title.md): [Relationship]
- [ADR-YYYY](./ADR-YYYY-title.md): [Relationship]
## References
- [Link to relevant documentation]
- [Link to research/analysis]
- [Link to similar decisions elsewhere]
---
## Review History
| Date | Reviewer | Decision |
|------|----------|----------|
| [Date] | [Name] | Proposed |
| [Date] | [Name] | Accepted |
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.
- 6d ago First seen · 224 lines · 10 tokens per session scan A db5b62cfd542
adr is a command published in the GitHub repository thapaliyabikendra/ai-artifacts (24 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 10 tokens to every session and 1,196 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.