Borrowing it
Nothing to install: this file belongs to forever-healthy/AI4L. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/forever-healthy/AI4L/main/.claude/skills/er/SKILL.mdgit clone --depth 1 https://github.com/forever-healthy/AI4LWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/forever-healthy/ai4l/er)<a href="https://agentmods.dev/skills/forever-healthy/ai4l/er"><img src="https://agentmods.dev/badge/skills/forever-healthy/ai4l/er.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.01083 |
| Opus 5 | $0.00010 | $0.00541 |
| Sonnet 5 | $0.00004 | $0.00217 |
| Haiku 4.5 | $0.00002 | $0.00108 |
Grade A, and why
er scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI4L - Main Skill for Evidence Review Creation and Auditing using @AGENTS
General Rules
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Parse the user's input to determine which sub-command to execute
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Set [args] to $ARGUMENTS
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Note the [start_time] when beginning any command, and report the [time_taken] when done
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All generated results go in [creation_dir] as .md files
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Do not edit or modify any files outside [creation_dir]
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Full lines formatted as
linecomments must be ignored when processing commands.
COMMAND: create {topic}
Create an evidence review (ER) using the @er-creator agent.
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If no [args] are given {
- Report:
usage: /er create {topic} - exit } otherwise { set [topic] to [args] }
- Report:
-
Report:
create: [topic] -
@er-creator:
[topic] -
Wait until the agent finishes
-
Report:
filename: [filename]
COMMAND: audit {er}
Audit an ER using the @er-auditor agent.
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If no [args] are given {
- Set [target_er] to the newest ER in [creation_dir] } else {
- Set [target_er] to [args] }
-
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]
COMMAND: fix {er}
Audit and fix an ER using the @er-fixer agent.
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If no [args] are given {
- Set [target_er] to the newest ER in [creation_dir] } else {
- Set [target_er] to [args] }
-
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]
COMMAND: combine {er}
Create a final QA file from all audits
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If no [args] are given {
- Set [target_er] to the newest ER in [creation_dir] } else {
- Set [target_er] to [args] }
-
Report:
combine: [target_er] -
@er-combiner:
[target_er] -
Wait until the agent finishes and returns the result
-
Report
QA file: [new_qa_filename]
COMMAND: iterate {er}
Loops audit/fix cycles up to [max_audits] times until [needed_passes] show 100% pass rate.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 161 lines · 19 tokens per session scan A 8f82f1dca2bf
er is a skill published in the GitHub repository forever-healthy/AI4L (38 stars, last pushed 11d ago), licensed MIT. It adds 19 tokens to every session and 1,083 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.
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