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ts-paper-data

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DATA-AWARE mode for the ts-paper suite. Activated by the orchestrator's router ONLY when the input ships REAL measured results (results_mode == "data_aware"); the proposal/no-numbers path is unchanged. Claude reads the user's data in ANY form (CSV / JSON / a pasted table / numbers in prose), judges it directly — there is NO fixed results schema — fills the result tables itself with real numbers in past tense, and writes the per-section text from the evidence. Two thin code backstops keep it honest: a schema-agnostic number-audit (draft_lint) and matplotlib results figures (plot_results.py). Use when a proposal/report comes with real experimental data.

skills/ts-paper-data/SKILL.md

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# ts-paper-data — write the paper FROM real results (Claude judges the data)

The proposal path forbids numbers and writes forward-looking ("we expect…"). DATA-AWARE mode inverts
that: the experiments are done, so report **what was measured, in past tense, with the real numbers**.
Per the project philosophy, **Claude reads and judges the data itself — no rigid `results.json` schema,
no loader.** Code does only the two irreducible things: machine-check that no number is fabricated, and
plot precise results figures.

## When this runs
Only when the router set `template.results_mode == "data_aware"` (the input had real results — a
file, a pasted table, or measured numbers in the prose). Otherwise the suite stays in proposal mode and
this skill is not used.

## Step 1 — read the data and write `results.facts.json` (the one artifact)
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