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 kennethkhoocy/applied-micro-skills --skill pyfixest-cupy64-absorbed-regressorsgit clone --depth 1 https://github.com/kennethkhoocy/applied-micro-skillsWrote 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/kennethkhoocy/applied-micro-skills/pyfixest-cupy64-absorbed-regressors)<a href="https://agentmods.dev/skills/kennethkhoocy/applied-micro-skills/pyfixest-cupy64-absorbed-regressors"><img src="https://agentmods.dev/badge/skills/kennethkhoocy/applied-micro-skills/pyfixest-cupy64-absorbed-regressors/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/kennethkhoocy/applied-micro-skills/pyfixest-cupy64-absorbed-regressors"><img src="https://agentmods.dev/badge/skills/kennethkhoocy/applied-micro-skills/pyfixest-cupy64-absorbed-regressors.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 | $0.00164 | $0.00729 |
| Opus 5 | $0.00082 | $0.00365 |
| Sonnet 5 | $0.00033 | $0.00146 |
| Haiku 4.5 | $0.00016 | $0.00073 |
Grade A, and why
pyfixest-cupy64-absorbed-regressors 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 12d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pyfixest cupy64 backend: absorbed regressors survive, reports change shape
Problem
Adding demeaner_backend="cupy64" to an existing pf.feols() call is treated
as a pure performance switch, but it changes the output: regressors with zero
within-FE identifying variation (fully absorbed by the fixed effects) that the
default numba backend silently drops are RETAINED by the cupy64 path (also in
its scipy/CPU fallback when cupy is not installed). They appear in the
coefficient table as non-identified garbage (huge coefficient, huge SE).
Context / Trigger Conditions
Observed 2026-07-18 (Specialist Directors US, H5 re-baseline audit): adding
the kwarg to 4 feols sites left the headline triple stable to 4 decimals
(B1 diff 1.45e-6, SE diff 9.9e-6) but the text report grew from 366 to 390
lines — 24 new rows for absorbed controls (e.g. event_x_lrisk = 435.8059,
SE 7106.3365) under firm-year + director FE, where firm-year-level
variables have no identifying variation.
Solution
- Treat a backend change as a POTENTIALLY OUTPUT-CHANGING edit: diff the report and expect schema changes, not byte equality. Verify the coefficients of interest at ~4-decimal precision instead.
- Never interpret retained absorbed-regressor rows as estimates; check within-FE variation before reading nuisance coefficients.
- Never parse pyfixest text reports by line position; anchor on the variable name of the coefficient you need.
- When byte-stable reports matter (regression-tested pipelines), pin the backend consistently everywhere rather than mixing backends across runs.
Verification
Re-run one model with and without the kwarg; compare: headline coef equal to ~1e-6, coefficient-row sets DIFFERENT (absorbed regressors present only under cupy64). That asymmetry confirms this behavior rather than a data change.
Notes
- Applies even with no GPU: the fail-open CPU fallback shows the same retention behavior, so "cupy isn't installed" does not make the kwarg inert.
- The headline inference (identified coefficients, clustered SEs) agrees to reporting precision; this is a report-shape/nuisance-row issue, not a correctness issue for identified estimates.
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.
- 12d ago First seen · 57 lines · 164 tokens per session scan A 4e63c7725dbf
pyfixest-cupy64-absorbed-regressors is a skill published in the GitHub repository kennethkhoocy/applied-micro-skills (27 stars, last pushed 7d ago), licensed MIT. It adds 164 tokens to every session and 729 once invoked, about $0.0008 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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