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.
npx agentmods add rules/rajitsaha/100xprism/dbgit clone --depth 1 https://github.com/rajitsaha/100xprismWrote 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/rules/rajitsaha/100xprism/db)<a href="https://agentmods.dev/rules/rajitsaha/100xprism/db"><img src="https://agentmods.dev/badge/rules/rajitsaha/100xprism/db.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.02217 |
| Opus 5 | $0.00013 | $0.01108 |
| Sonnet 5 | $0.00005 | $0.00443 |
| Haiku 4.5 | $0.00003 | $0.00222 |
Grade A, and why
db scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DB — Universal Database Access
Reads connection config from the project instruction file (CLAUDE.md, AGENTS.md, .cursorrules, or equivalent) or ~/.claude/db-connections.json (global registry).
Scope:
/dbexecutes specific SQL or migrations against named connections. For analytics in plain English, use/query.
Supported engines
cloud-sql | postgres | snowflake | databricks | athena | presto | oracle
Usage
/db— default audit query for current project DB/db "SELECT count(*) FROM users"— arbitrary SQL on current project DB/db migrate— run pending migrations/db prod-snowflake— named connection from global registry/db prod-snowflake "SELECT ..."— named connection + custom SQL
Step 0 — Parse arguments
# If first arg looks like a connection name (no spaces, no SQL keywords), treat as named connection
ARGS="${1:-}"
if echo "$ARGS" | grep -qE '^[a-zA-Z0-9_-]+$' && ! echo "$ARGS" | grep -qiE '^(SELECT|INSERT|UPDATE|DELETE|CREATE|DROP|ALTER|SHOW|DESCRIBE|migrate)'; then
NAMED_CONNECTION=$(echo "$ARGS" | awk '{print $1}')
SQL=$(echo "$ARGS" | cut -s -d' ' -f2-)
else
NAMED_CONNECTION=""
SQL="$ARGS"
fi
Step 1 — Load connection config
# Detect project instruction file
INSTRUCTION_FILE=$(ROOT=$(git rev-parse --show-toplevel 2>/dev/null); for f in CLAUDE.md AGENTS.md .cursorrules; do [ -f "$ROOT/$f" ] && echo "$ROOT/$f" && break; done)
DB_CONNECTIONS="$HOME/.claude/db-connections.json"
if [ -n "$NAMED_CONNECTION" ]; then
ENGINE=$(python3 -c "import json; d=json.load(open('$DB_CONNECTIONS')); c=d.get('$NAMED_CONNECTION',{}); print(c.get('engine',''))" 2>/dev/null)
CONFIG_SOURCE="registry:$NAMED_CONNECTION"
elif [ -n "$INSTRUCTION_FILE" ] && grep -q "^engine:" "$INSTRUCTION_FILE" 2>/dev/null; then
ENGINE=$(grep "^engine:" "$INSTRUCTION_FILE" | head -1 | cut -d: -f2 | tr -d ' ')
CONNECTION_NAME=$(grep "^connection:" "$INSTRUCTION_FILE" | head -1 | cut -d: -f2 | tr -d ' ')
CONFIG_SOURCE="instruction-file"
elif [ -f "$DB_CONNECTIONS" ]; then
echo "No DB config found in project instruction file. Available connections:"
python3 -c "
import json
d = json.load(open('$DB_CONNECTIONS'))
for i, (name, cfg) in enumerate(d.items(), 1):
print(f' {i}) {name} ({cfg.get(\"engine\",\"unknown\")})')
"
read -rp "Select connection (number or name): " SELECTION
NAMED_CONNECTION=$(python3 -c "
import json, sys
d = json.load(open('$DB_CONNECTIONS'))
keys = list(d.keys())
sel = '$SELECTION'
if sel.isdigit() and 1 <= int(sel) <= len(keys):
print(keys[int(sel)-1])
elif sel in d:
print(sel)
else:
print('', end='')
" 2>/dev/null)
ENGINE=$(python3 -c "import json; d=json.load(open('$DB_CONNECTIONS')); print(d.get('$NAMED_CONNECTION',{}).get('engine',''))" 2>/dev/null)
CONFIG_SOURCE="registry:$NAMED_CONNECTION"
else
echo "ERROR: No database config found."
echo " Option 1: Add a '## Database' section to your project instruction file"
echo " Option 2: Create ~/.claude/db-connections.json with named connections"
exit 1
fi
if [ -z "$ENGINE" ]; then
echo "ERROR: Could not determine database engine from config."
exit 1
fi
echo "Engine: $ENGINE | Config: $CONFIG_SOURCE"
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.
- today First seen · 200 lines · 26 tokens per session scan A ce8e6908252b
db is a cursor rule published in the GitHub repository rajitsaha/100xprism (10 stars, last pushed 4d ago), licensed MIT. It adds 26 tokens to every session and 2,217 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 2026-09-03.
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