onboard-report

onboard-report is a skill for Claude Code, Codex from vbcherepanov/total-agent-memory. It costs 65 tokens per session (670 once invoked), scanned B, original, MIT.

An employee-onboarding reporting tool for viewing training progress, lesson completion, quiz scores, and curriculum activity across departments.

In plain words
What is it for?
It is for producing onboarding reports, reviewing individual progress, grading open quiz answers, and managing department lessons and quizzes.
Why use it?
It gives department heads and approved company viewers one place to see who is progressing, stuck, or waiting for quiz grading.

Skill for Claude CodeCodex

Written for Claude Code and Codex: argument-hint in frontmatter, but also agents/openai.yaml present.

Part of the total-agent-memory plugin — 6 skills, 7 hooks, 2 MCP servers shipped together

Good fit It is for producing onboarding reports, reviewing individual progress, grading open quiz answers, and managing department lessons and quizzes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vbcherepanov/total-agent-memory/onboard-report
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add vbcherepanov/total-agent-memory --skill onboard-report
Clone the repo
git clone --depth 1 https://github.com/vbcherepanov/total-agent-memory

Made for: Claude Code, Codex.

Or install total-agent-memory, the plugin that ships this one along with the rest of its 6 skills, 7 hooks, 2 MCP servers.

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 onboard-report

README.md
[![agentmods](https://agentmods.dev/badge/skills/vbcherepanov/total-agent-memory/onboard-report/github.svg)](https://agentmods.dev/skills/vbcherepanov/total-agent-memory/onboard-report)
Your own site
<a href="https://agentmods.dev/skills/vbcherepanov/total-agent-memory/onboard-report"><img src="https://agentmods.dev/badge/skills/vbcherepanov/total-agent-memory/onboard-report/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 onboard-report

Your own site · 80×15
<a href="https://agentmods.dev/skills/vbcherepanov/total-agent-memory/onboard-report"><img src="https://agentmods.dev/badge/skills/vbcherepanov/total-agent-memory/onboard-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 670 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. A grade says what 26 rules found in the file — not that it is safe.
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 $0.00065 $0.00670
Opus 5.5 $0.00026 $0.00268
Sonnet 5.5 $0.00013 $0.00134
Haiku 4.5 $0.00006 $0.00067

Measured 29d ago against content hash 07a4c4fd88d0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade B, and why

onboard-report scanned grade B with 1 finding 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 29d 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.

Asks the agent to reveal its instructionsmediumSystem prompt leakage

Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.

For each item in `grading_queue`, show prompt, rubric and answer, ask the head
skills/onboard-report/SKILL.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/onboard-report — onboarding results and curriculum

Tools are named onboarding_* (possibly client-prefixed). The server enforces access: department heads (team role manager) see and manage their own department; company_viewer and superadmin see every department; superadmin may also manage. A Forbidden error means the user lacks that role — say so.

1. Pick the department

Use $ARGUMENTS if given. Otherwise call onboarding_overview and choose from viewable_teams (report) or manageable_teams (editing).

2. Report

Call onboarding_team_report(team_id) and present:

  • a table: employee × module with status (not started / in progress / quiz available / awaiting review / passed / failed), lessons done, best quiz score, and the relevant date (started, finished, passed);
  • who is stuck (failed with no attempts left, or no activity) and lessons flagged as updated since people studied them (summary.updated_lessons);
  • the latest entries of log in plain language.

For one person's detail: onboarding_progress(team_id, user_id).

3. Grade open answers (department head)

For each item in grading_queue, show prompt, rubric and answer, ask the head for points (0..points) and an optional comment, then call onboarding_grade(attempt_id, question_id, score, comment). Grading the last open answer finalizes the attempt. Never grade without the head's decision.

4. Build or edit the curriculum (department head)

  1. onboarding_curriculum_get(team_id) → current curriculum and revision.
  2. For a first version, onboarding_curriculum_draft(team_id) groups the department's memory into modules/lessons (LLM-polished only if the server has one). Review it with the head: reorder, rename, remove, add lesson text.
  3. Save with onboarding_curriculum_set(team_id, expected_revision, curriculum). Keep existing ids so employees keep their progress; omitted items are archived.
  4. Quizzes per module: onboarding_quiz_draft(module_id) (needs a server LLM; drafts are checked against lesson quotes) or write questions with the head, then onboarding_quiz_set(module_id, expected_revision, questions). Question types: single, multiple (zero-based correct indexes), open (needs rubric). Set pass_threshold and max_attempts per module in the curriculum.

Read the full file on GitHub · 59 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 29d ago First seen · 59 lines · 65 tokens per session scan B 07a4c4fd88d0

Subscribe to this mod's changes

onboard-report is a skill published in the GitHub repository vbcherepanov/total-agent-memory (73 stars, last pushed yesterday), licensed MIT. It adds 65 tokens to every session and 670 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-29.

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