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/sakrut/ai-code-graph/view-modelsgit clone --depth 1 https://github.com/sakrut/ai-code-graphWrote 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/sakrut/ai-code-graph/view-models)<a href="https://agentmods.dev/commands/sakrut/ai-code-graph/view-models"><img src="https://agentmods.dev/badge/commands/sakrut/ai-code-graph/view-models.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 | $0.00255 |
| Opus 5 | $0.00000 | $0.00128 |
| Sonnet 5 | $0.00000 | $0.00051 |
| Haiku 4.5 | $0.00000 | $0.00026 |
Grade A, and why
view-models 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 yesterday.
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.
This is a copy
97% identical to view-models — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
View Models View current AI model configuration.
Model Configuration Display
Shows the currently configured AI providers and models for Task Master.
Execution
task-master models
Information Displayed
-
Main Provider
- Model ID and name
- API key status (configured/missing)
- Usage: Primary task generation
-
Research Provider
- Model ID and name
- API key status
- Usage: Enhanced research mode
-
Fallback Provider
- Model ID and name
- API key status
- Usage: Backup when main fails
Visual Status
Task Master AI Model Configuration
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Main: ✅ claude-3-5-sonnet (configured)
Research: ✅ perplexity-sonar (configured)
Fallback: ⚠️ Not configured (optional)
Available Models:
- claude-3-5-sonnet
- gpt-4-turbo
- gpt-3.5-turbo
- perplexity-sonar
Next Actions
Based on configuration:
- If missing API keys → Suggest setup
- If no research model → Explain benefits
- If all configured → Show usage tips
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.
- yesterday First seen · 52 lines · 0 tokens per session scan A 0a146ba61394
view-models is a command published in the GitHub repository sakrut/ai-code-graph (3 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 255 tokens. A static security scan graded it A with 0 findings. It is 97% identical to view-models, differing in 5 lines, and is treated as a copy.
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.