check

A check-back command for an active learning worksheet. It grades the task, saves the result to a knowledge graph, and reports the change in experience points.

In plain words
What is it for?
Use it to review a completed task, record what was learned for a topic, and see the resulting XP change.
Why use it?
It removes the need to manually review the worksheet, update stored learning records, and calculate the result.

Command

Part of the ramp plugin — 11 commands, 2 hooks shipped together

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.

agentmods
npx agentmods add commands/gf-labs/ramp/check
Clone the repo
git clone --depth 1 https://github.com/gf-labs/ramp

Or install ramp, the plugin that ships this one along with the rest of its 11 commands, 2 hooks.

Per session 19 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,643 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. Scan, not verified.
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 $0.00019 $0.01643
Opus 5 $0.00010 $0.00822
Sonnet 5 $0.00004 $0.00329
Haiku 4.5 $0.00002 $0.00164

Measured 2d ago against content hash 7147e1875a2f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

check 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 2d 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.

Reads agent configuration directoriesmediumAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

!`TOPIC=$(sed -nE 's/^\*Skill tree: ([A-Za-z0-9_-]+).*/\1/p' .ramp/worksheet.md 2>/dev/null | head -1); TOPIC="${TOPIC:-claude-code}"; cat "$HOME/.claude/ramp/graphs/$TOPIC.md" 2>/dev/null || echo "NO_TREE_FILE:$TOPIC"`
commands/check.md · 99 lines

How it starts

The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Context

Your note (optional): $ARGUMENTS

Today's date: !date +%Y-%m-%d

Repo name (for the evidence trail): !basename "$(git rev-parse --show-toplevel 2>/dev/null || pwd)"

Active worksheet (./.ramp/worksheet.md): !cat .ramp/worksheet.md 2>/dev/null || echo "NO_WORKSHEET"

Active topic (from the worksheet's Skill tree: line, else claude-code): !TOPIC=$(sed -nE 's/^\*Skill tree: ([A-Za-z0-9_-]+).*/\1/p' .ramp/worksheet.md 2>/dev/null | head -1); echo "${TOPIC:-claude-code}"

Knowledge graph contents (for the active topic — the tree the save applies to): !TOPIC=$(sed -nE 's/^\*Skill tree: ([A-Za-z0-9_-]+).*/\1/p' .ramp/worksheet.md 2>/dev/null | head -1); TOPIC="${TOPIC:-claude-code}"; cat "$HOME/.claude/ramp/graphs/$TOPIC.md" 2>/dev/null || echo "NO_TREE_FILE:$TOPIC"

Kernel CLI (the no-MCP write path): ![ -n "$CLAUDE_PLUGIN_ROOT" ] && [ -f "$CLAUDE_PLUGIN_ROOT/ramp_core.py" ] && echo "KERNEL_OK" || echo "KERNEL_UNAVAILABLE"

Lesson registry (./.ramp/lessons.md): !cat .ramp/lessons.md 2>/dev/null || echo "NO_LESSONS"


Your role

Check-back: grade the active task, persist the result through the validated writer, and report the XP delta. This file is the canonical check-back protocolup.md's natural-language done handler runs the same protocol inline; if you change one, change both.

A demonstration only counts when it reaches the graph through the writer (save_graph — never-downgrade, XP recomputed in code). Never record it by editing the graph file directly, and never just acknowledge in chat — persist, then report what changed.

Step 1 — Locate the task

If the worksheet is NO_WORKSHEET: say "No active task — nothing to check. Run /ramp:up to get one." Stop.

From the worksheet, read the node title (the # heading), the Goal (the mastery criterion), and the Task. If the graph is NO_TREE_FILE or contains no node line matching the title: say the worksheet doesn't match the saved graph (stale workspace) and suggest /ramp:up to regenerate it. Stop.

Read the full file on GitHub · 99 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. 2d ago First seen · 99 lines · 19 tokens per session scan B 7147e1875a2f

Subscribe to this mod's changes

check is a command published in the GitHub repository gf-labs/ramp (2 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 1,643 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.