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 skills/buildmoonshot/skillpacks/arcpy-safe-editsnpx skills add buildmoonshot/skillpacks --skill arcpy-safe-editsgit clone --depth 1 https://github.com/buildmoonshot/skillpacksWrote 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/buildmoonshot/skillpacks/arcpy-safe-edits)<a href="https://agentmods.dev/skills/buildmoonshot/skillpacks/arcpy-safe-edits"><img src="https://agentmods.dev/badge/skills/buildmoonshot/skillpacks/arcpy-safe-edits.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 | $0.00072 | $0.00525 |
| Opus 5 | $0.00036 | $0.00262 |
| Sonnet 5 | $0.00014 | $0.00105 |
| Haiku 4.5 | $0.00007 | $0.00052 |
Grade A, and why
arcpy-safe-edits 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 5d 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.
What it actually says
ArcPy Safe Edits
Editing an enterprise geodatabase is not editing a file. Versioning, locks, and related data make "just run an UpdateCursor" a way to corrupt production. Edit safely.
Use an edit session
- Wrap edits in
arcpy.da.Editor(workspace)withstartEditing()/startOperation(). An edit session is required for versioned data, and for data participating in topologies, geometric/utility networks, relationship classes, or attribute rules. - Choose the right mode:
startEditing(with_undo, multiuser_mode)—multiuser_mode=Truefor versioned SDE,Falsefor nonversioned. - On error,
abortOperation()/stopEditing(save_changes=False); onlystopEditing(True)after success. Wrap in try/except so a failure rolls back instead of leaving a half-applied edit.
Manage cursors and locks
- Use
arcpy.da.UpdateCursor/InsertCursorinside the edit session, always with awithblock (ordelthe cursor) so it releases its lock. A leaked cursor holds a lock that blocks every other editor. - Don't hold a schema lock longer than needed; ensure no ArcGIS Pro session or other process has the dataset open when you need exclusive access.
Respect versioning
- Edit the correct version, not
DEFAULTdirectly in a multiuser workflow. Understand the reconcile/post cycle; leave reconcile/post andCompressto the established workflow (often a DBA task), don't improvise them.
Respect data integrity
- Honor domains, subtypes, and attribute rules — writing an out-of-domain value or bypassing a rule corrupts data quietly.
- Test on a copy or a non-default version first. Never debug edit logic against production DEFAULT.
Why this matters
A naive cursor edit on versioned SDE can deadlock other editors, leave a half-applied transaction, or violate referential integrity across related classes — and the damage is multiuser and hard to undo. Edit sessions, scoped locks, and the right version turn risky writes into safe, atomic ones.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 33 lines · 72 tokens per session scan A 3501f03a0000
arcpy-safe-edits is a skill published in the GitHub repository buildmoonshot/skillpacks (2 stars, last pushed 2mo ago), licensed MIT. It adds 72 tokens to every session and 525 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
lamindb
Use when working with LaminDB, the open-source lineage-native lakehouse for biological datasets and models. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation with Bionty, collections, branches, storage, and workflow integrations.
tiledbvcf
Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
defining-cohort-phenotypes
Authors computable phenotype and cohort definitions in the OHDSI ATLAS / CIRCE style over the OMOP CDM, combining standard concept sets with NLP-derived features that OpenMed extracts. Use when the user wants to define a patient cohort, write a computable phenotype, reuse PheKB or OHDSI Phenotype Library logic, build…
benchling-integration
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
chembl-database
Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures. Use when the user asks about compounds, targets, IC50/Ki values, drug mechanisms, or structure searches.
nvalchemi-data-storage
How to write, read, compose, and load atomic data using nvalchemi's composable Zarr-backed storage pipeline (Writer, Reader, Dataset, MultiDataset, DataLoader). Use when saving simulation outputs or trajectories to disk, converting structures (e.g. ASE / extxyz) into Zarr stores, assembling datasets for training or…