check
FreeProject Verification & Consistency Checking
The English summary is being prepared. Showing the original description.
AI4L - Main Skill for Evidence Review Creation and Auditing using @AGENTS
The English summary is being prepared. Showing the original description.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Parse the user's input to determine which sub-command to execute
Set [args] to $ARGUMENTS
Note the [start_time] when beginning any command, and report the [time_taken] when done
All generated results go in [creation_dir] as .md files
Do not edit or modify any files outside [creation_dir]
Full lines formatted as line comments must be ignored when processing commands.
No ER found for: [args] and exitCreate an evidence review (ER) using the @er-creator agent.
If no [args] are given {
usage: /er create {topic}Report: create: [topic]
@er-creator: [topic]
Wait until the agent finishes
Report: filename: [filename]
Audit an ER using the @er-auditor agent.
Resolve [target_er] from [args] (see "Resolving the target ER")
Report: audit: [target_er]
@er-auditor: [target_er]
Wait until the agent finishes and returns the result
Report: target_er: [target_er]
Report: pass_rate: [pass_rate]
Report: audit_filename: [audit_filename]
Audit and fix an ER using the @er-fixer agent.
Resolve [target_er] from [args] (see "Resolving the target ER")
Report: fix: [target_er]
@er-fixer: [target_er]
Wait until the agent finishes and returns the result
Report: target_er: [target_er]
Report: pass_rate: [pass_rate]
Report: audit_filename: [audit_filename]
Create a final _ER_QA.md file from all audits
Resolve [target_er] from [args] (see "Resolving the target ER")
Report: combine: [target_er]
@er-combiner: [target_er]
Wait until the agent finishes and returns the result
Set [new_qa_filename] to the filename the agent returns in its QA file: line
Report QA file: [new_qa_filename]
Loops audit/fix cycles up to [max_audits] times until [needed_passes] show 100% pass rate.
Resolve [target_er] from [args] (see "Resolving the target ER")
Report: iterate: [target_er]
Initialize {
Loop while [iteration] < [max_audits] and [consecutive_passes] < [needed_passes] {
@er-fixer: [target_er]
Wait until the agent finishes and returns the result
If the [pass_rate] is 100%, increment [consecutive_passes]; otherwise reset to 0
Increment [iteration]
Report: "Iteration [iteration]: Pass rate = [pass_rate] ([consecutive_passes]/[needed_passes] consecutive passes needed)" }
@er-combiner: [target_er]
Wait until the agent finishes and returns the result
Set [new_qa_filename] to the filename the agent returns in its QA file: line
Report QA file: [new_qa_filename]
If [consecutive_passes] >= [needed_passes] {
Return: status: success
} else {
Return: status: failed
}
Return: target_er: [target_er]
Return: iterations: [iteration]
A create and multi-pass audit workflow.
If no [args] are given {
usage: /er full {topic}Execute the "create" command
Execute the "iterate" command with [filename] returned by "create" as [args]
Compares all ERs for a given intervention, typically from different AI models or versions, to determine which is strongest based on content quality and the latest QA audit results.
If no [args] are given {
Report: compare: [intervention]
Compare all ERs in [creation_dir] with a similar [intervention] in their frontmatter
Present a clear recommendation of which ER is strongest and why.
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Project Verification & Consistency Checking
The English summary is being prepared. Showing the original description.