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 skills add Kevin-Liu-01/Agent-Machines --skill column-aligned-fieldsgit clone --depth 1 https://github.com/Kevin-Liu-01/Agent-MachinesWrote 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/skills/kevin-liu-01/agent-machines/column-aligned-fields)<a href="https://agentmods.dev/skills/kevin-liu-01/agent-machines/column-aligned-fields"><img src="https://agentmods.dev/badge/skills/kevin-liu-01/agent-machines/column-aligned-fields/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kevin-liu-01/agent-machines/column-aligned-fields"><img src="https://agentmods.dev/badge/skills/kevin-liu-01/agent-machines/column-aligned-fields.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.00606 |
| Opus 5 | $0.00022 | $0.00303 |
| Sonnet 5 | $0.00009 | $0.00121 |
| Haiku 4.5 | $0.00004 | $0.00061 |
Grade A, and why
column-aligned-fields 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 10d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Column-Aligned Field Comments
When a dataclass, enum, or similar structured type has inline comments on its fields, align
all comments to the same column and wrap the block with # fmt: off / # fmt: on so the
formatter preserves the alignment.
Rules
- The comment column starts two spaces after the longest value expression in the group.
- Every inline comment is a complete sentence ending with a period.
- Fields without comments leave the comment column empty (no filler).
# fmt: offgoes on the line immediately before the decorator or class statement.# fmt: ongoes on the line immediately after the last field.
Example
# fmt: off
@dataclass
class RawFieldSpec:
"""A single field extracted from a provider's OpenAPI schema."""
provider: str # Provider id, e.g. "openai".
name: str # Upstream field name as-is.
type_str: str # JSON Schema type string.
required: bool # True when in the required array.
description: str | None = None
constraints: JSONObject = dataclass_field(default_factory=dict)
union: UnionInfo | None = None # Discriminated union info.
items: ArrayItemsInfo | None = None # Array item type info.
additional_properties: JSONObject | bool | None = None # additionalProperties schema.
variant_metadata: list[VariantMeta] | None = None # Titled union variant metadata.
# fmt: on
# fmt: off
class TransferCode(IntEnum):
"""Transfer type codes for billing.transfers.code column."""
USAGE = 1 # Standard usage charge.
BALANCE_DEPOSIT = 100 # Stripe payment or balance top-up.
CREDIT_ADJUSTMENT = 101 # Manual credit adjustment.
CREDIT_GRANT = 102 # Promotional or signup credits.
API_USAGE = 200 # LLM API token usage.
MCP_BUYER_CHARGE = 400 # Buyer charge for MCP tool call.
MCP_SELLER_HOLD = 410 # Seller pending hold (7-day window).
MCP_SELLER_CLAWBACK = 411 # Reverse posted hold on refund.
# fmt: on
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.
- 10d ago First seen · 62 lines · 43 tokens per session scan A c5124038b3d7
column-aligned-fields is a skill published in the GitHub repository Kevin-Liu-01/Agent-Machines (26 stars, last pushed 9d ago), licensed MIT. It adds 43 tokens to every session and 606 once invoked, about $0.0002 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 skills, from other repositories
dd-code-generation
Use pup CLI for immediate Datadog operations or generate code for integration into applications.
python
Python development with ruff, mypy, pytest - TDD and type safety.
data-analysis-jupyter
Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
environment-setup
Use when Python environment setup is needed for data visualization or conda installation is required.
tao-run-on-virtualenv
Run a Python training/eval script directly in an existing local virtualenv — no docker, no container. Implements the four-verb consumer contract (submit/status/logs/cancel) over a vendored process-lifecycle runner with durable on-disk state, PID-reuse-safe identity, and process-group cleanup. Use for docker-free local…
python-memory-safe-scripts
Memory-safe Python script patterns for long-running processes under systemd MemoryMax constraints. Covers allocator purge (mimalloc/glibc malloctrim), HTTP response lifecycle, DataFrame cleanup, thread-local connection reuse, and periodic GC cadence. Battle-tested through 5 OOM optimization cycles on production GPU…