audience-sim
Skill4 starsChanged 3 months agoApache-2.0View on GitHub
Simulate a defined reader or viewer encountering prose, screenplay, storyboard, animatic, or rough-cut material for the first time, reporting felt attention, questions, character legibility, clarity, and payoff without turning into craft critique.
In this repository139 other files it gives its agents.
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 13
Read REVIEW_PROTOCOLS.md and the exact material being tested.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Audience Sim
Workflow
- Read
REVIEW_PROTOCOLS.mdand the exact material being tested. - Define medium, audience persona, genre familiarity, and the knowledge boundary before the scope begins. If the user did not specify a persona, state the narrow assumption used.
- Move through the material in order. Track attention, curiosity, predictions, emotional alignment, character understanding, and flow.
- For visual material, also track geography, action readability, eyelines, and whether a cut or transition causes confusion.
- Anchor notable reactions to a scene, beat, shot, paragraph, or timestamp when available.
- Report the experience. Do not rewrite the artifact and do not pretend one simulated persona represents every audience.
- Save durable notes under
00_project/reviews/when they feed a later revision decision.
Done
The report states who the simulated audience is, what they knew, where their experience changed, and what questions or expectations remained at the end.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.