jet-literature-positioning — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited jet-literature-positioning (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.
JET spans general theory, so referees are expert in the nearest existing results, not a broad empirical literature. Positioning is theorem-relative, not topic-relative. For each closest paper, state the delta in one of these precise forms:
it to [weaker condition]."
environments; ours covers [the general case]."
domain] in this environment; Theorem 1 does."
theorems as the comparison set.
name-year / author-year, so elsarticle-harv is the safest LaTeX default.
Build a private table before writing related work:
| Closest theorem | Assumptions | Object/result | Your delta | Where proved |
|---|---|---|---|---|
| Paper A, theorem X | finite types, single crossing, etc. | existence/characterization/bound | weaker/general/new constructive result | Theorem 1 / Proposition 2 |
Only the strongest two or three rows belong in the manuscript. The table prevents vague "we extend" language and makes overclaiming visible before a referee catches it.
The comparison set differs by JET area; find the closest theorem inside the right lineage, name the lineage in one clause, then jump straight to the single nearest result:
deltas weaken the common prior, risk neutrality, or transferable utility.
deltas relax substitutability or add contracts, constraints, or distributional objectives.
smooth ambiguity); deltas trade one axiom for a behaviorally weaker one.
senders, dynamics, or robustness to the receiver's beliefs.
alter monitoring, commitment, or the discounting structure.
The closest result is [Author (Year), Theorem k], which proves [object] under [assumption set S].
Our Theorem 1 [weakens S to S' / covers the general (non-quasi-linear / infinite-type) case /
characterizes an object their analysis leaves open]. The techniques also differ: their argument
relies on [tool]; ours requires [new tool] because [what breaks under the weaker assumptions].
Relative to the applied literature on [topic], our contribution is the theorem itself, not a
new application of existing results.A hypothetical paper characterizes optimal disclosure when the receiver is maxmin. Two candidate anchors compete: the standard persuasion concavification theorem (delta: receiver ambiguity breaks Bayesian updating, so the sender's value is no longer a concavification) and the robust-mechanism literature (delta: the designer commits to information rather than transfers). Write both rows in the theorem-delta table, then lead the related-work paragraph with the anchor whose assumptions you actually weaken — the other becomes one supporting sentence, not a co-headline.
【Closest result】<author, year, the theorem>
【Our delta】weaker assumption | greater generality | new object | tighter/constructive
【Stated as】"<one-sentence positioning to put in the intro>"
【Abstract cites】kept minimal + written in full? [Y/N]
【Next】jet-identification-strategy (assumptions & proof) / jet-contribution-framing~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.