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/causify-ai/helpers/coding.make_function_privatenpx skills add causify-ai/helpers --skill coding.make_function_privategit clone --depth 1 https://github.com/causify-ai/helpersWhat 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 | $0.00715 |
| Opus 5 | $0.00008 | $0.00358 |
| Sonnet 5 | $0.00003 | $0.00143 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
coding.make_function_private 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 2d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
For each function <FUNC> in the passed Python file <FILE>, determine if it
should be private or public by checking if it's called by Python files or
Jupyter notebooks outside <FILE>
Definition: External Files
-
External files = any Python file or Jupyter notebook outside the target file
- Including:
- Sibling scripts in the same package (e.g.,
notes_to_pdf.pycalling functions fromlib_notes_to_pdf.py) - Parent/sibling modules that import from the target
- CLI entry point scripts
- Sibling scripts in the same package (e.g.,
- Excluding
- Test files that test the target module, since a function is private even if it's called by a test files
- Including:
-
Note: Functions that form the public API of a library module should be PUBLIC, even if they're not called from a
__main__entry point. If a module exports utility functions for use by other scripts in the package, those are PUBLIC functions
Private Functions
-
If the function
<FUNC>is not called by any external file, then it should be a private function and should be renamed and prepended with a_- E.g.,
def function->def _function - Include only internal utility functions, helpers, and implementation details
- E.g.,
-
Modify all the callers of the function
<FUNC>to use the new name -
Example: Private Functions
# lib_helper.py def public_function(): _internal_helper() # Only called within this file → should be private def _internal_helper(): pass
Public Functions
-
If the function
<FUNC>is called by external files, then it should be a public function and its name should NOT start with_- This includes functions in shared utility modules called by sibling scripts
-
Modify all callers (both external and internal) of the function to use the new name
-
Example: Public Functions
# lib_notes_to_pdf.py def preprocess_notes(file_name, prefix): # Called by notes_to_pdf.py → PUBLIC _internal_step_1(file_name) # Private helper def _internal_step_1(file_name): # Only used within lib_notes_to_pdf.py → PRIVATE pass
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.
- 2d ago First seen · 79 lines · 16 tokens per session scan A 90871acf0e16
coding.make_function_private is a skill published in the GitHub repository causify-ai/helpers (145 stars, last pushed 2d ago), licensed Apache-2.0. It adds 16 tokens to every session and 715 once invoked, about $0.0001 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.
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