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.
git clone --depth 1 https://github.com/crabsmadethis/d2r-horadric-toolsWrote 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/commands/crabsmadethis/d2r-horadric-tools/d2r-scan)<a href="https://agentmods.dev/commands/crabsmadethis/d2r-horadric-tools/d2r-scan"><img src="https://agentmods.dev/badge/commands/crabsmadethis/d2r-horadric-tools/d2r-scan/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.
<a href="https://agentmods.dev/commands/crabsmadethis/d2r-horadric-tools/d2r-scan"><img src="https://agentmods.dev/badge/commands/crabsmadethis/d2r-horadric-tools/d2r-scan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00012 | $0.00302 |
| Opus 5 | $0.00006 | $0.00151 |
| Sonnet 5 | $0.00002 | $0.00060 |
| Haiku 4.5 | $0.00001 | $0.00030 |
Grade A, and why
d2r-scan 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 11d 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.
What it actually says
D2R Scanner
Run the d2rdoctor scanner on $ARGUMENTS.
python3 << 'PYEOF'
import sys
sys.path.insert(0, '<parent-of-d2r-tools>')
from d2r_scanner import run_scanner
target = '$ARGUMENTS'.strip().lower() if '$ARGUMENTS'.strip() else 'all'
run_scanner(target)
PYEOF
Interpreting Results
- Hard error (returns False): Deployment blocker. Fix before loading in D2R.
- Checksum MISMATCH: Recalculate with
calc_checksum()before writing. - LF INCONSISTENT: Merc items present but lf_count=0. Remove merc items or fix count.
- Ghost character:
.ctlexists but.d2smissing. Restore from backup or delete support files. - Encoding issues: Wrong ext bits. Rebuild with corrected
build_item(). - Grid view: Each cell shows first char of item type.
.= free slot.
Rules
- Rule 4: Always run scanner after every edit
- Rule 17: Scanner hard errors are deployment blockers — never dismiss without bit-level proof
- Rule 19: Investigate every remaining error before closing
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.
- 11d ago First seen · 34 lines · 12 tokens per session scan A de3864bd42a9
d2r-scan is a command published in the GitHub repository crabsmadethis/d2r-horadric-tools (4 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 302 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
add-tool
Build a new UEFN Toolbelt tool named "$ARGUMENTS". Follow the tool-developer agent workflow exactly.
publish-check
Run a full pre-publish audit. Check everything before the user submits their island to Fortnite.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.