Local Budget
v1.0.0Analyze exported bank/credit card CSV files locally to track spending, categorize transactions with LLM reasoning, compare against user-defined budgets, and...
⭐ 0· 66·0 current·0 all-time
byNew Age Investments@newageinvestments25-byte
MIT-0
Download zip
LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (local budget, CSV parsing, categorization, report generation) match the included files and arguments. All required functionality is implemented by the provided Python scripts; no unrelated credentials, binaries, or services are requested.
Instruction Scope
SKILL.md instructs the agent to run the local scripts in scripts/ and to review/adjust LLM-suggested categories before reporting. The instructions reference only CSVs, the included reference files, and optional local budget files; they do not ask the agent to read other system files, environment variables, or to send data to external endpoints.
Install Mechanism
No install spec; this is instruction + included Python scripts. The scripts use only Python standard library modules (csv, json, re, datetime, collections) and do not download or extract remote archives or install third-party packages.
Credentials
The skill declares no required environment variables, credentials, or config paths. The included code does not access external secrets or call external services. Category rules mention vendor names including 'openai'/'chatgpt' only for pattern matching (to label subscription charges), which is proportionate to the task.
Persistence & Privilege
always is false and the skill does not attempt to persist settings across the agent or modify other skills. It only reads/writes user-specified CSV/JSON/markdown files in the local filesystem (as directed by the user).
Assessment
This skill appears coherent and local-only, but review these practical safety steps before installing: 1) Run the scripts locally on a machine you trust (they process potentially sensitive bank CSVs). 2) Inspect the included Python files yourself if you have doubts — there are no network calls or credential use, so a quick code review is sufficient. 3) When running, keep outputs (categorized.json, report.md) in a directory you control; avoid writing reports into shared/system folders unless intended. 4) The workflow relies on a manual LLM-review step (the agent or you should confirm low/medium confidence items) — do not skip manual review if you care about correct categorization. 5) If your agent platform can autonomously call skills and transmit data externally, ensure agent-level network/external-access policies are appropriate; the skill itself does not perform network exfiltration. If you want higher assurance, run the scripts in a disposable environment (virtualenv / container) and test with redacted sample CSVs first.Like a lobster shell, security has layers — review code before you run it.
latestvk97d8gzyw24v2m1ewwx1qj9c1983mphx
License
MIT-0
Free to use, modify, and redistribute. No attribution required.
