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/pixeltable/pixeltable-skill/scaffoldgit clone --depth 1 https://github.com/pixeltable/pixeltable-skillWrote 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/pixeltable/pixeltable-skill/scaffold)<a href="https://agentmods.dev/commands/pixeltable/pixeltable-skill/scaffold"><img src="https://agentmods.dev/badge/commands/pixeltable/pixeltable-skill/scaffold.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 | $0.00016 | $0.00265 |
| Opus 5 | $0.00008 | $0.00133 |
| Sonnet 5 | $0.00003 | $0.00053 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
scaffold scanned grade A with 1 finding 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 today.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
The last argument (`my_app`) is a catalog name, not a folder on disk. Try the app: insert, curl a route, or `pxt dashboard`. Pass `-f` when there is no TTY. What it actually says
Start a Pixeltable project with the CLI. Then edit app.py.
Arguments: $ARGUMENTS
Steps:
- Pick a fresh directory. Install, mark the project root, and write a working file:
mkdir myapp && cd myapp
pip install 'pixeltable[serve]'
pxt init
pxt service example --out app.py
Schema only (no HTTP): pxt schema example --brief --out app.py.
- Create tables, then start HTTP:
pxt schema update app.py my_app
pxt service update app.py my_app -f
pxt service list
The last argument (my_app) is a catalog name, not a folder on disk. Try the app: insert, curl a route, or pxt dashboard. Pass -f when there is no TTY.
-
Extra features (RAG, video, agents, a UI) are added in
app.py. Start from the example file. Do not invent a secondpxt schema updatepath. -
State the directory you created and the commands you ran.
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.
- today Changed · +1 lines · +2 tokens per session b3218ed2bf2c
- 2d ago Changed · -14 lines · -2 tokens per session 74ed374111b6
- 4d ago First seen · 49 lines · 16 tokens per session scan A 602d67081489
scaffold is a command published in the GitHub repository pixeltable/pixeltable-skill (5 stars, last pushed yesterday), licensed Apache-2.0. It adds 16 tokens to every session and 265 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). 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.