Skill: epicoun/ai4l (version 1.0.0)

--- name: er description: AI4L - Enabling everyone to use AI to generate high-quality, evidence-based reviews of interventions aimed at optimizing health and longevity. licence: see LICENSE.md --- Copyright (c) 2026 [Forever Healthy Foundation](https://forever-healthy.org) # AI4L Evidence Review Toolkit Version: 2026.03.19.1 This skill handles all required Evidence Review workflows. ## Key Files * `AI4L.md` — The QA audit checklist for ERs * `./results/` — The folder where all generated ERs, audits, and fixes are saved as .md files ## General Rules * Parse the user's input to determine which command to execute * Note the start time (HH:MM:SS) when beginning any command, and report the time taken when done * NEVER use sub-agents unless explicitly instructed to do so by the command instructions. * If a sub-agent is needed, the command instructions will specify exactly what to tell the sub-agent. * All generated results go in `./results/` as .md files * Do not edit or modify any files outside `./results/` * Set [default_topic] to “Using Telmisartan to Improve Health and Longevity” ## Command: CREATE Trigger: "create" Create an evidence review (ER) on a given topic, or on the [default_topic] if no topic is specified. ### Process #### Topic Parsing * Set [remainder] to the rest of the input after the command trigger word "create" * If [remainder] is empty, set [topic] to the [default_topic] * If [remainder] looks like "Using \<intervention> for/as/to \<goal>", set [topic] to [remainder] * If [remainder] contains only an intervention and no goal, set [topic] to "Using \<intervention> to Improve Health and Longevity" #### ER Creation * Notify the user that an ER will be created for [topic] * Create an ER for [topic] that can pass a QA audit as described in "AI4L.md" * Save the result as an .md file in `./results/` using the filename given in the result * Report the filename and location when done. ## Command: SUBAUDIT (audit using sub-agent) Trigger: "subaudit" Audit an ER using a sub-agent ### Process #### Determine the Target * If no further information is given, set [target] to the last evidence review generated; otherwise, take the remainder of the input as [target] * If [target] = "all", audit all "ER.md" files that have not been audited yet using the instructions in "AI4L.md" #### Do an actual Audit using a sub-agent Launch a sub-agent with Opus as its model to audit the ER using the prompt below. DO NOT pass any instructions to the sub-agent other than the prompt. > * State your model name and version number to the user > * Audit the [target] file using the instructions in "AI4L.md" > * Do NOT use any sub-agents for the task. Do things step-by-step. > * Save the result in `./results/` using the name defined in the result > * Do not modify any files outside `./results/` #### Report and Offer Fix * Read the audit output and report the pass rate * If not 100%, and the audit was done by the same AI model that generated the ER, ask the user if they want to fix it * If yes, read the audit file, identify all failed items, and fix the ER based on the auditor's comments. DO NOT modify the QA file. Only the ER may be edited during the fix step. ## Command: AUDIT (no sub-agent) Trigger: "audit" Audit an ER without using a sub-agent ### Process #### Determine the Target * If no further information is given, set [target] to the last evidence review generated; otherwise, take the remainder of the input as [target] * If [target] = "all", audit all "ER.md" files that have not been audited yet using the instructions in "AI4L.md" #### Do the actual audit * Audit [target] using the instructions in "AI4L.md" * Do NOT use any sub-agents for the task. Do things step-by-step. * Save the result in `./results/` using the name defined in the result * Do not modify any files outside `./results/` #### Report and Offer Fix * Read the audit output and report the pass rate * If the pass rate is not 100%, and the audit was done by the same AI model that generated the ER, ask the user if they want to fix it * If yes, fix the ER based on the audit results. DO NOT modify the QA file. Only the ER may be edited during the fix step. ## Command: ITERATE Trigger: "iterate" Loops audit/fix cycles up to 10 times until two consecutive audits show 100% pass rate. ### Process * If no further information is given, set [target] to the last evidence review generated; otherwise, take the remainder of the input as [target] #### Audit loop Initialize: `iteration = 0`, `consecutive_passes = 0`, `max_iterations = 10` Loop while `iteration < max_iterations` and `consecutive_passes < 2`: { * Audit [target] using the instructions in "AI4L.md" * Do NOT use any sub-agents for the audit. Do things step-by-step. * If the pass rate is 100%, increment `consecutive_passes`; otherwise reset to 0 * If the pass rate is not 100%, identify all failed items, fix the ER * Increment `iteration` * Report: "Iteration {n}: Pass rate = {rate}% ({consecutive_passes}/2 consecutive passes needed)" } #### Report - **Success:** "Pipeline complete. The ER passed two consecutive audits with a 100% pass rate after {n} iteration(s)." - **Limit reached:** "Pipeline stopped after 10 iterations. Best pass rate achieved: {rate}%. The latest ER and audit files are in ./results/ for manual review." List all files generated in `./results/`. ## Command: FULL Trigger: "FULL" Run a complete create and multi-pass audit workflow ### Process * execute the CREATE command * execute the ITERATE command ## Command: COMPARE Trigger: "COMPARE" Compare 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. ### Process * If no further information is given, set [intervention] to the intervention of the latest "ER.md" in "./results/" (parse the filename to extract the intervention) * Otherwise, take the remainder of the input as [intervention] * Compare all [intervention] "ER.md" files by the quality of the content. Be detailed. Also, take into account the latest [intervention] "QA.md" for each. * Present a clear recommendation of which ER is strongest and why.


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