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make-content-source-to-units

Step of the make-content playbook: mines one finished asset into scored quotable units and maps a different lead unit to each platform. Run make-content instead; not for direct use.

Install / Use

npx skills add gooseworks-ai/goose-skills --skill make-content-source-to-units

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

81/100

Supported Platforms

Universal

Tags

Our assessment of make-content-source-to-units

make-content-source-to-units scores 81/100 on our quality scale, 3548th of 4,581 Development & Engineering skills we index.

Its SKILL.md is 2.8 KB long, split into 3 sections with 1 code example: a solid amount of guidance for an agent.

With 1,222 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
12/20
Description
15/15
Adoption
13/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 33 days ago, so make-content-source-to-units is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

make-content-source-to-units compared with similar skills

All 4 of these similar skills score higher than make-content-source-to-units; compare them before choosing.

SkillScoreStarsUpdatedFormat
make-content-source-to-units (this skill)by gooseworks-ai811.2k33d agoSKILL.md
taste-skillby Leonxlnx100102.4ktodayCLAUDE.md
ai-job-searchby ai-job-search-dev10065.7k4d agoCLAUDE.md
claude-howtoby luongnv8910045.0k5d agoCLAUDE.md
algorithmic-artby anthropics100177.9k33d agoSKILL.md

Frequently asked questions

How do I install make-content-source-to-units?
Run npx skills add gooseworks-ai/goose-skills --skill make-content-source-to-units. The install tabs above show the steps for each supported agent.
Which AI agents does make-content-source-to-units work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is make-content-source-to-units safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is make-content-source-to-units still maintained?
The repository was last updated 33 days ago, so make-content-source-to-units is actively maintained.

name: make-content-source-to-units description: >- Step of the make-content playbook: mines one finished asset into scored quotable units and maps a different lead unit to each platform. Run make-content instead; not for direct use. version: 0.1.1

Source to units

Input: the full text or transcript of one finished asset (../make-content/references/repurposing.md §1 says how to get it in). Read it completely before mining. Never mine from a summary.

Output table

Source: <title> · <link or media_asset_id> · <length/duration>
| # | Type | Anchor (verbatim, ≤25 words) | Where (timestamp / ¶) | Alone | Charge | Novel | Proof | Total | Best homes |
|---|------|------------------------------|-----------------------|-------|--------|-------|-------|-------|------------|
  • Type: claim · contrarian take · story · stat · quote · framework/steps · mistake+fix · before/after · objection+answer · audience Q · demo moment.
  • Scores (1–3):
    • Alone: makes sense without the rest of the source.
    • Charge: surprise, tension, humour or stakes.
    • Novel: says what competitors don't.
    • Proof: 3 if an approved claim or a real number in the source backs it; 1 if it is opinion only. Opinion is fine for a contrarian take, but it must be the speaker's.
  • Mine 15–30 units from a long source and 5–10 from a short one. Drop units scoring ≤6.
  • A short or silent clip (a 10–30s product video, often with an empty transcript) holds 2–4 units: its caption, its on-screen text, and what the scenes show (media_analyze gives visible_text and the scene descriptions). Say so plainly: it supports 2–3 versions at most, not "every platform". Ask whether a longer asset exists (a founder interview, a webinar, an article) before stretching a thin source into filler.
  • A stat in the source is still a claim. If it is about the brand and not on the approved list, keep the unit but mark "stat needs approval". The claims check will cut or soften it.
  • Spoken quotes stay verbatim, attributed to the speaker as named in the source. Never tidy a quote. On-screen text that came from OCR (visible_text) is text, not a quote: fix obvious OCR errors and never put it in quote marks.
  • Sources under ~30s: rank the units instead of applying the ≤6 cut-off, and keep the top 2–4. If fewer than 3 units score 7 or more, recommend a richer asset before writing, naming one from the brand's own posts if you can (e.g. one with speech). Then let the human choose.

Then map

For each platform the brand uses, pick the lead unit by the map in ../make-content/references/repurposing.md §3. No two versions share a lead unit. When strong units outnumber platforms, a platform can take two surfaces (e.g. carousel + Reel). If the source has fewer strong units than the brand has platforms, say so and make fewer, better versions rather than filler.

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryDevelopment
Updated1mo ago
Forks208

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions