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.fix_param_usenpx skills add causify-ai/helpers --skill coding.fix_param_usegit 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 | $0.00359 |
| Opus 5 | $0.00008 | $0.00179 |
| Sonnet 5 | $0.00003 | $0.00072 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
coding.fix_param_use 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.
What it actually says
-
I will pass you a file
-
In that file, make sure that:
- Callers pass the parameters by position and pass the keywords arguments
- Constant should be assigned to intermediate variable with the same name corresponding to the formal parameters
-
For a function with the signature
def apply_llm_prompt_to_df( prompt: str, df: pd.DataFrame, extractor: Callable[[Union[str, pd.Series]], str], target_col: str, batch_mode: str, *, model: str, batch_size: int = 50, dump_every_batch: str = "", tag: str = "Processing", testing_functor: Optional[Callable[[str], str]] = None, use_sys_stderr: bool = False, ) -> Tuple[pd.DataFrame, Dict[str, int]]: -
Bad
df, stats = hllmcli.apply_llm_prompt_to_df( prompt=prompt, df=df, extractor=extract_person_industry_from_df, target_col="industry", batch_mode=batch_mode, batch_size=batch_size, model=model, tag=tag, ) -
Good
target_col = "industry" df, stats = hllmcli.apply_llm_prompt_to_df( prompt, df, extract_person_industry_from_df, target_col, batch_mode, batch_size=batch_size, model=model, tag=tag, )
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 · 59 lines · 16 tokens per session scan A 02ee9722348a
coding.fix_param_use 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 359 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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