car-methods — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited car-methods (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
CAR welcomes any appropriate method; the bar is fit and rigor, not a preferred method. Pick the design that earns the claim:
| Claim | Design that earns it |
|---|---|
| Capital-market/contracting effect of reporting | Panel archival with fixed effects + an identification strategy |
| Causal effect of an information feature on judgment | Controlled experiment (lab/online/professional subjects) |
| Existence/optimality of an equilibrium or contract | Analytical model: primitives, equilibrium concept, proofs |
| Mechanism inside firms, audits, or standard-setting | Field study / interviews with an explicit coding protocol |
| A new construct's measurement and external validity | Survey with a validated instrument; or multi-method |
A two-study design (e.g., an experiment isolating the mechanism behind an archival association) is a recognized CAR strength.
For the empirical / causal lane, estimate and audit rather than only specify. Full map: execution-with-mcp. CAR is archival/empirical accounting; the DiD / IV / RDD chain serves its causal designs around reporting and regulation.
detect_design → recommend → fit with as_handle=true → audit_result toenumerate the checks the design owes.
callaway_santanna / sun_abraham + bacon_decomposition+ honest_did_from_result. IV: effective_f_test + anderson_rubin_ci. RDD: rdrobust + mccrary_test.
romano_wolf for the many-outcomefamily-wise correction reviewers expect.
Match the toolchain to the reviewer pool, and report the effect size the venue wants. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.
【Design】panel-archival / experiment / analytical / field / survey / multi-method
【Inference fit】each prediction supportable? notes ...
【Identification / internal validity / equilibrium】strategy + assumptions ...
【Ethics】REB/IRB clearance, exemption, or senior-admin letter secured?
【Instrument & proprietary data】full instrument; verification/NDA plan ...
【Next step】car-data-analysis~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.