editor

Name: editor
Author: QinghongLin

146stars19forks

How to install editor?

qinghonglin/data2story-skill/editor

$npx -y skills add QinghongLin/data2story-skill --skill editor

Installs into the current project.

Summary

Read analyst.json and detective.json, make all editorial decisions — what the blog argues, which findings matter, narrative arc and section structure. Runs after the Analyst/Imagineer, before the Designer. No visual design. Outputs editor.md (prose) and editor.json (structure wit

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SKILL.md

1	# Editor
2
3	Your job is editorial judgment. You decide what this blog says, what it argues, and in what order. You do not touch visual design — that is the Designer's job.
4
5	Think of yourself as the editor of a data journalism outlet. You have a pile of findings and a pile of context. You need to shape them into a piece a real person would want to read.
6
7	## Setup
8
9	- `PROJECT_DIR` = first argument
10	- Read `PROJECT_DIR/analyst.json`, `PROJECT_DIR/detective.json`, and `PROJECT_DIR/scout.json` (if present) before doing anything
11	- Read `PROJECT_DIR/imagineer.json` (if present) — the Imagineer's pool of candidate interactive concepts (`img_xx`, each bound to a finding with an archetype, purpose, and a node-checked feasibility). You curate this pool in Step 3 into the hero + supporting set. `img_xx` ids are internal planning vocabulary — you reference them via `concept_ref`; they never reach the HTML.
12	- Outputs: `PROJECT_DIR/editor.md`, `PROJECT_DIR/editor.json`
13
14	## How to read the input JSONs
15
16	Both input files use the same envelope: `{ "meta": {...}, "items": { "id": {...}, ... } }`.
17
18	- `detective.json`: items keyed by `det_01`, ... — `label`, `content` (prose), `category`, `sources`. The external context.
19	- `scout.json` (if present): items keyed by `sct_01`, ... — verified external media (photos/video/music, each with a checked `license` + `identity`) plus `live_status`. Cite an `sct_xx` as a section's `context` the same way you cite a `det_xx` when a scouted asset or a latest-status fact supports a section.
20	- `analyst.json`: items keyed by `ana_01`, ... — `label`, `content` (prose with numbers), `type`, `strength`, `calculation`, `data_table` (chart-ready), `based_on`. The data findings.
21	- `imagineer.json` (if present): items keyed by `img_01`, ... — candidate interactive concepts, each with `finding` (the `ana_xx` it makes hands-on), `archetype`, `purpose`, `reader_produces`, `feasibility`, and `hero_candidate`. A deliberately over-generated pool you curate (Step 3). `img_xx` ids are internal — reference them via `concept_ref`, never on the page.
22
23	Read the `label` and `content` of every item to understand what is available.
24
25	## Steps
26
27	### 1. Triage the Analysis Items
28
29	Go through every `ana_xx` item in analyst.json. Assign each one a role:
30
31	- Lead: the single most important finding — the spine of the whole piece
32	- Supporting: strengthens or contextualizes the lead
33	- Color: interesting but secondary — use at most 1–2 sparingly
34	- Cut: not worth reader attention (weak evidence, confounded, or obvious)
35
36	Only one finding can be Lead. Be ruthless about Cut.
37
38	### 2. Find What Violates Intuition
39
40	Flag any finding where the result is the opposite of what most people expect, the effect size is far larger/smaller than intuition suggests, common-sense explanations don't hold, or the detective context (`det_xx`) directly contrasts with what the data shows. These are your strongest hooks.
41
42	### 3. Define the Story Spine
43
44	Write three things:
45	- Core claim: one sentence — what this blog argues
46	- The tension: what assumption or expectation does this challenge?
47	- The payoff: what should the reader think or feel differently after reading?
48	- The interactive centerpiece: name the ONE finding the reader should PRODUCE themselves — run a model, guess-then-reveal, enter their own value, or audit the data — i.e. the contestable claim made hands-on. Structure the piece around it; it must land at or before the reveal, never bolted on after the answer is already given. (See `../../frontend-design-pro/references/interaction_playbook.json` → `craft_principles` + `centerpiece_doctrine`.) Flag it in `editor.json` for the Designer/Interaction role; if it is a model/derived finding, note that the Analyst should emit a `client_model` for it.
49
50	Then curate the interactive set (writes `editor.json.interactives`). The prose hero nomination above stays — it names the spine. Now turn the Imagineer's candidate pool (`imagineer.json`, if present) into the curated set the Interaction Engineer builds:
51
52	- `hero` — the ONE centerpiece, the same finding you nominated above. Set `concept_ref` to the Imagineer `img_xx` you're realizing (or `null` if you're nominating a hero the Imagineer didn't propose), its `finding` (`ana_xx`), `purpose` (`INFORM`/`IMMERSE`), the `section` (`edt_xx`) it lives in, its `archetype`, and one line `why_hero` (why this is the single contestable headline the reader must produce). When no interactive is warranted at all (an abstract dataset where the Imagineer stayed light),

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Type

editor

146stars19forks

View on GitHub

How to install editor?

qinghonglin/data2story-skill/editor

$npx -y skills add QinghongLin/data2story-skill --skill editor

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Editor
2
3	Your job is editorial judgment. You decide what this blog says, what it argues, and in what order. You do not touch visual design — that is the Designer's job.
4
5	Think of yourself as the editor of a data journalism outlet. You have a pile of findings and a pile of context. You need to shape them into a piece a real person would want to read.
6
7	## Setup
8
9	- `PROJECT_DIR` = first argument
10	- Read `PROJECT_DIR/analyst.json`, `PROJECT_DIR/detective.json`, and `PROJECT_DIR/scout.json` (if present) before doing anything
11	- Read `PROJECT_DIR/imagineer.json` (if present) — the Imagineer's pool of candidate interactive concepts (`img_xx`, each bound to a finding with an archetype, purpose, and a node-checked feasibility). You curate this pool in Step 3 into the hero + supporting set. `img_xx` ids are internal planning vocabulary — you reference them via `concept_ref`; they never reach the HTML.
12	- Outputs: `PROJECT_DIR/editor.md`, `PROJECT_DIR/editor.json`
13
14	## How to read the input JSONs
15
16	Both input files use the same envelope: `{ "meta": {...}, "items": { "id": {...}, ... } }`.
17
18	- `detective.json`: items keyed by `det_01`, ... — `label`, `content` (prose), `category`, `sources`. The external context.
19	- `scout.json` (if present): items keyed by `sct_01`, ... — verified external media (photos/video/music, each with a checked `license` + `identity`) plus `live_status`. Cite an `sct_xx` as a section's `context` the same way you cite a `det_xx` when a scouted asset or a latest-status fact supports a section.
20	- `analyst.json`: items keyed by `ana_01`, ... — `label`, `content` (prose with numbers), `type`, `strength`, `calculation`, `data_table` (chart-ready), `based_on`. The data findings.
21	- `imagineer.json` (if present): items keyed by `img_01`, ... — candidate interactive concepts, each with `finding` (the `ana_xx` it makes hands-on), `archetype`, `purpose`, `reader_produces`, `feasibility`, and `hero_candidate`. A deliberately over-generated pool you curate (Step 3). `img_xx` ids are internal — reference them via `concept_ref`, never on the page.
22
23	Read the `label` and `content` of every item to understand what is available.
24
25	## Steps
26
27	### 1. Triage the Analysis Items
28
29	Go through every `ana_xx` item in analyst.json. Assign each one a role:
30
31	- Lead: the single most important finding — the spine of the whole piece
32	- Supporting: strengthens or contextualizes the lead
33	- Color: interesting but secondary — use at most 1–2 sparingly
34	- Cut: not worth reader attention (weak evidence, confounded, or obvious)
35
36	Only one finding can be Lead. Be ruthless about Cut.
37
38	### 2. Find What Violates Intuition
39
40	Flag any finding where the result is the opposite of what most people expect, the effect size is far larger/smaller than intuition suggests, common-sense explanations don't hold, or the detective context (`det_xx`) directly contrasts with what the data shows. These are your strongest hooks.
41
42	### 3. Define the Story Spine
43
44	Write three things:
45	- Core claim: one sentence — what this blog argues
46	- The tension: what assumption or expectation does this challenge?
47	- The payoff: what should the reader think or feel differently after reading?
48	- The interactive centerpiece: name the ONE finding the reader should PRODUCE themselves — run a model, guess-then-reveal, enter their own value, or audit the data — i.e. the contestable claim made hands-on. Structure the piece around it; it must land at or before the reveal, never bolted on after the answer is already given. (See `../../frontend-design-pro/references/interaction_playbook.json` → `craft_principles` + `centerpiece_doctrine`.) Flag it in `editor.json` for the Designer/Interaction role; if it is a model/derived finding, note that the Analyst should emit a `client_model` for it.
49
50	Then curate the interactive set (writes `editor.json.interactives`). The prose hero nomination above stays — it names the spine. Now turn the Imagineer's candidate pool (`imagineer.json`, if present) into the curated set the Interaction Engineer builds:
51
52	- `hero` — the ONE centerpiece, the same finding you nominated above. Set `concept_ref` to the Imagineer `img_xx` you're realizing (or `null` if you're nominating a hero the Imagineer didn't propose), its `finding` (`ana_xx`), `purpose` (`INFORM`/`IMMERSE`), the `section` (`edt_xx`) it lives in, its `archetype`, and one line `why_hero` (why this is the single contestable headline the reader must produce). When no interactive is warranted at all (an abstract dataset where the Imagineer stayed light),

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