burn

burn is a command for Claude Code from avelikiy/great_cto. It costs 33 tokens per session (1,670 once invoked), scanned A, original, MIT.

A monitoring command that measures how quickly a service is using its SLO budget across several time windows. An SLO is a target for service reliability.

In plain words
What is it for?
Use it with recorded digest snapshots to calculate burn rates, estimate remaining runway, and flag services at risk.
Why use it?
It can reveal fast or slow reliability deterioration before the allowed failure budget is exhausted.

Command for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: model in frontmatter.

Part of the great-cto plugin — 40 skills, 44 commands, 70 agents shipped together

Good fit Use it with recorded digest snapshots to calculate burn rates, estimate remaining runway, and flag services at risk.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/avelikiy/great_cto/burn
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.

Clone the repo
git clone --depth 1 https://github.com/avelikiy/great_cto

Made for: Claude Code.

Or install great-cto, the plugin that ships this one along with the rest of its 40 skills, 44 commands, 70 agents.

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 burn

README.md
[![agentmods](https://agentmods.dev/badge/commands/avelikiy/great_cto/burn/github.svg)](https://agentmods.dev/commands/avelikiy/great_cto/burn)
Your own site
<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.

agentmods 80×15 button for burn

Your own site · 80×15
<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>
Per session 33 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,670 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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 $0.00033 $0.01670
Opus 5 $0.00016 $0.00835
Sonnet 5 $0.00007 $0.00334
Haiku 4.5 $0.00003 $0.00167

Measured 5d ago against content hash 6343173bef02, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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.

commands/burn.md · 144 lines

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

Read the full file on GitHub · 144 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. 5d ago First seen · 144 lines · 33 tokens per session scan A 6343173bef02

Subscribe to this mod's changes

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.