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/avelikiy/great_ctoWrote 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/avelikiy/great_cto/burn)<a href="https://agentmods.dev/commands/avelikiy/great_cto/burn"><img src="https://agentmods.dev/badge/commands/avelikiy/great_cto/burn/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/avelikiy/great_cto/burn"><img src="https://agentmods.dev/badge/commands/avelikiy/great_cto/burn.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 | $0.00033 | $0.01670 |
| Opus 5 | $0.00016 | $0.00835 |
| Sonnet 5 | $0.00007 | $0.00334 |
| Haiku 4.5 | $0.00003 | $0.00167 |
Grade A, and why
burn scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Burn-Rate aggregator. Compute SLO budget burn rate across multiple windows from .great_cto/slo-burn-history.log (snapshot per /digest run). Alert on bad trends before the budget is exhausted.
Multi-window pattern from Google SRE: a single point-in-time read can't tell you if you're burning fast or slow. By comparing snapshots over different windows, fast burns surface immediately, slow burns surface within a day, and projected exhaustion gives you actionable runway.
Setup
source .great_cto/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
HISTORY=.great_cto/slo-burn-history.log
CACHE=.great_cto/slo-budget-current.md
FILTER="${1:-}"
if [ ! -f "$HISTORY" ]; then
echo "No burn history yet — run /digest at least once to seed the snapshot log."
echo "(Burn rate needs at least 2 snapshots to compute a derivative.)"
exit 0
fi
LINES=$(grep -cv "^[[:space:]]*#" "$HISTORY" 2>/dev/null || echo 0)
if [ "$LINES" -lt 2 ]; then
echo "Only 1 snapshot in burn history — need at least 2. Run /digest again tomorrow."
exit 0
fi
Compute burn rates per service+SLI
python3 - "$HISTORY" "$FILTER" <<'PY'
import sys, datetime, collections, re
path, flt = sys.argv[1], sys.argv[2]
# Read snapshots → per (service, sli) list of (ts_epoch, used_min, budget_min, pct)
series = collections.defaultdict(list)
with open(path) as f:
for line in f:
line = line.strip()
if not line or line.startswith('#'): continue
parts = [p.strip() for p in line.split('|')]
if len(parts) < 6: continue
ts_iso, svc, sli, used_s, budget_s, pct_s = parts[:6]
if flt and svc != flt: continue
try:
ts = datetime.datetime.fromisoformat(ts_iso.replace('Z', '+00:00')).timestamp()
used = float(used_s); budget = float(budget_s); pct = int(pct_s)
except Exception:
continue
series[(svc, sli)].append((ts, used, budget, pct))
if not series:
msg = f"No snapshots match '{flt}'." if flt else "No parseable snapshots."
print(msg); sys.exit(0)
now = datetime.datetime.utcnow().timestamp()
# Normal monthly burn = budget / 30 days = budget per second / (30*86400)
# Burn rate multiplier = (delta_used / delta_seconds) / (budget / (30*86400))
def find_snapshot_at_or_before(snaps, target_ts):
"""Return the latest snapshot <= target_ts (or earliest if none qualify)."""
candidates = [s for s in snaps if s[0] <= target_ts]
return candidates[-1] if candidates else snaps[0]
WINDOWS = [
("24h", 86400, 14.4, "🔴 page"),
("7d", 604800, 6.0, "⚠ ticket"),
("30d", 2592000, 1.0, "ℹ review"),
]
print("═══ SLO Burn Rate ═══")
print()
SERVICES = sorted(series.keys())
for (svc, sli) in SERVICES:
snaps = sorted(series[(svc, sli)])
latest = snaps[-1]
ts_now, used_now, budget, pct = latest
if budget <= 0:
continue
age_hours = (now - ts_now) / 3600.0
print(f"{svc} / {sli}")
print(f" Budget: {used_now:.1f}min used / {budget:.1f}min total ({pct}% consumed)")
if age_hours > 36:
print(f" ⚠ latest snapshot is {age_hours:.0f}h old — run /digest to refresh")
# Normal burn rate (per second) = budget consumed if you burn evenly across 30d
normal_per_s = budget / (30 * 86400)
fired = []
for label, secs, threshold, action in WINDOWS:
target = ts_now - secs
prev = find_snapshot_at_or_before(snaps, target)
delta_used = used_now - prev[1]
delta_secs = ts_now - prev[0]
if delta_secs <= 0:
print(f" {label}: insufficient history")
continue
actual_per_s = delta_used / delta_secs
multiplier = actual_per_s / normal_per_s if normal_per_s > 0 else 0
burned_pct = (delta_used / budget) * 100 if budget > 0 else 0
marker = "🔴" if multiplier >= threshold else ("⚠ " if multiplier >= threshold/2 else "✓ ")
print(f" {label:>4}: {burned_pct:5.1f}% of budget ({multiplier:5.2f}× normal) {marker}")
if multiplier >= threshold:
fired.append((label, multiplier, action))
# Projected exhaustion at current 7d rate (if positive burn)
target_7d = ts_now - 604800
prev_7d = find_snapshot_at_or_before(snaps, target_7d)
delta_7d_used = used_now - prev_7d[1]
delta_7d_secs = ts_now - prev_7d[0]
remaining_min = budget - used_now
if delta_7d_secs > 0 and delta_7d_used > 0 and remaining_min > 0:
burn_per_day = delta_7d_used / (delta_7d_secs / 86400)
days_left = remaining_min / burn_per_day
print(f" Projected exhaustion: {days_left:.1f} days at current 7d pace")
elif remaining_min <= 0:
print(f" ⚠⚠ EXHAUSTED — freeze feature deploys, see references/reliability.md")
else:
print(f" Projected exhaustion: ∞ (no burn in window)")
if fired:
worst = max(fired, key=lambda x: x[1])
print(f" → ALERT: {worst[2]} — {worst[0]} burn = {worst[1]:.1f}× normal")
print()
print("─────────────────────────")
print("Thresholds (Google SRE multi-window): 24h ≥ 14.4× → page | 7d ≥ 6× → ticket | 30d ≥ 1× → review")
print("Snapshots are written by /digest. Increase digest frequency for finer-grained alerts.")
PY
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.
- 5d ago First seen · 144 lines · 33 tokens per session scan A 6343173bef02
burn is a command published in the GitHub repository avelikiy/great_cto (89 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 1,670 once invoked, about $0.0002 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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