AI4L: Skill for Claude Code

.claude/skills/er/SKILL.md

er is a skill for Claude Code from forever-healthy/AI4L. It costs 19 tokens per session (1,308 once invoked), scanned A, original, MIT.

A tool for creating and checking evidence reviews, which are written summaries that assess research findings on a topic. It saves generated files in a chosen folder and uses separate agents for creation and auditing.

In plain words
What is it for?
Use it to create an evidence review about a topic or audit an existing review file.
Why use it?
It gives a defined process for producing a review and then checking it, while limiting changes to the specified output folder.

Skill for Claude Code

Written for Claude Code: $ARGUMENTS substitution.

This is forever-healthy/AI4L's own configuration. It tells Claude Code how to work on AI4L itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything AI4L configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/forever-healthy/AI4L/main/.claude/skills/er/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/forever-healthy/AI4L

Made for: Claude Code.

Wrote 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.

agentmods badge for er

README.md
[![agentmods](https://agentmods.dev/badge/skills/forever-healthy/ai4l/er/github.svg)](https://agentmods.dev/skills/forever-healthy/ai4l/er)
Your own site
<a href="https://agentmods.dev/skills/forever-healthy/ai4l/er"><img src="https://agentmods.dev/badge/skills/forever-healthy/ai4l/er/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for er

Your own site · 80×15
<a href="https://agentmods.dev/skills/forever-healthy/ai4l/er"><img src="https://agentmods.dev/badge/skills/forever-healthy/ai4l/er.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,308 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00019 $0.01308
Opus 5.5 $0.00008 $0.00523
Sonnet 5.5 $0.00004 $0.00262
Haiku 4.5 $0.00002 $0.00131

Measured 12d ago against content hash c9a6b492114c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 12d 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.

.claude/skills/er/SKILL.md · 165 lines

How it starts

The opening of the file, as written. The whole thing — 165 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

  • 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.

Resolving the target ER

  • If no [args] are given, set [target_er] to the newest _ER.md file in [creation_dir]
  • Otherwise, if [args] names an existing _ER.md file in [creation_dir] (with or without the directory), set [target_er] to that file's bare filename, without any directory
  • Otherwise treat [args] as an intervention: list the _ER.md files in [creation_dir] whose filename starts with [args] converted the way 6.25 of [er_prompt] converts short_topic (lowercase, spaces and dashes to underscores, other characters removed), and set [target_er] to the newest of them by the date in the filename
  • If nothing matches, report No ER found for: [args] and exit

COMMAND: create {topic}

Create an evidence review (ER) using the @er-creator agent.

  • If no [args] are given {

    • Report: usage: /er create {topic}
    • exit } otherwise { set [topic] to [args] }
  • 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.

  • 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]

COMMAND: fix {er}

Audit and fix an ER using the @er-fixer agent.

  • Resolve [target_er] from [args] (see "Resolving the target ER")

  • Report: fix: [target_er]

Read the full file on GitHub · 165 lines

Changes

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.

  1. 12d ago Changed · +2 lines c9a6b492114c
  2. 14d ago Changed · +2 lines f9409de34723
  3. 1mo ago Changed 5b2d71005b8f
  4. 1mo ago Changed c5024a1d142d
  5. 1mo ago Changed 09907c7c930c
  6. 1mo ago Changed ae55c33e4a5d
  7. 1mo ago Changed 39a3cdad6ceb
  8. 1mo ago Changed 9db76fa23eb9
  9. 1mo ago Changed b7568607aafc
  10. 2mo ago First seen · 161 lines · 19 tokens per session scan A 8f82f1dca2bf

Subscribe to this mod's changes

er is a skill published in the GitHub repository forever-healthy/AI4L (43 stars, last pushed 10d ago), licensed MIT. It adds 19 tokens to every session and 1,308 once invoked, about $0.0001 per session on Opus 5.5. 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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