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 skills add hamzabellouch/agent-skills --skill carbon-accounting-ghg-protocolgit clone --depth 1 https://github.com/hamzabellouch/agent-skillsWrote 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/skills/hamzabellouch/agent-skills/carbon-accounting-ghg-protocol)<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/carbon-accounting-ghg-protocol"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/carbon-accounting-ghg-protocol/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/skills/hamzabellouch/agent-skills/carbon-accounting-ghg-protocol"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/carbon-accounting-ghg-protocol.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 | $0.00089 | $0.01038 |
| Opus 5.5 | $0.00036 | $0.00415 |
| Sonnet 5.5 | $0.00018 | $0.00208 |
| Haiku 4.5 | $0.00009 | $0.00104 |
Grade A, and why
carbon-accounting-ghg-protocol 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 18d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Carbon Accounting & GHG Protocol Skill Guide
This skill governs mathematical modeling, emission factor ingestion, and audit trail generation for enterprise carbon accounting engines compliant with the GHG Protocol.
1. Greenhouse Gas Scope Architecture
The GHG Protocol categorizes greenhouse gas emissions into three operational scopes:
[ Global Corporate Boundary ]
|
|-- SCOPE 1 (Direct Emissions)
| |-- Stationary combustion (boilers, natural gas)
| +-- Mobile fleet combustion (gasoline/diesel company vehicles)
|
|-- SCOPE 2 (Indirect Energy Emissions)
| |-- Location-based: Regional grid average factor (eGRID / IEA)
| +-- Market-based: Supplier-specific contracts / Renewable Energy Certificates (RECs)
|
+-- SCOPE 3 (Value Chain Indirect)
|-- Category 1: Purchased Goods and Services
|-- Category 6: Business Travel (flights, hotels)
+-- Category 7: Employee Commuting & Remote Work
2. Production Code Implementation (Python)
A. Carbon Footprint Calculation Engine
from decimal import Decimal
from typing import Literal
from pydantic import BaseModel
class EmissionFactor(BaseModel):
activity_unit: str # e.g. "kWh", "liter_diesel", "passenger_km"
co2e_factor_kg: Decimal # kg CO2e per activity unit
source: str # e.g. "EPA 2024", "DEFRA 2023"
class ActivityRecord(BaseModel):
id: str
facility_id: str
scope: Literal["SCOPE_1", "SCOPE_2", "SCOPE_3"]
activity_type: str
quantity: Decimal
unit: str
class CarbonLedgerEngine:
def __init__(self, factors: dict[str, EmissionFactor]):
self.factors = factors
def calculate_emission_kg(self, record: ActivityRecord) -> Decimal:
factor_key = f"{record.activity_type}_{record.unit}"
factor = self.factors.get(factor_key)
if not factor:
raise ValueError(f"No emission factor found for {factor_key}")
# Total kg CO2e = Activity Quantity * Emission Factor
co2e_kg = record.quantity * factor.co2e_factor_kg
return co2e_kg
def compute_facility_summary(self, records: list[ActivityRecord]) -> dict:
summary = {
"scope_1_mt": Decimal("0.0"),
"scope_2_mt": Decimal("0.0"),
"scope_3_mt": Decimal("0.0"),
"total_co2e_metric_tonnes": Decimal("0.0"),
}
for r in records:
kg = self.calculate_emission_kg(r)
metric_tonnes = kg / Decimal("1000.0")
if r.scope == "SCOPE_1":
summary["scope_1_mt"] += metric_tonnes
elif r.scope == "SCOPE_2":
summary["scope_2_mt"] += metric_tonnes
elif r.scope == "SCOPE_3":
summary["scope_3_mt"] += metric_tonnes
summary["total_co2e_metric_tonnes"] = (
summary["scope_1_mt"] + summary["scope_2_mt"] + summary["scope_3_mt"]
)
return summary
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.
- 18d ago First seen · 110 lines · 89 tokens per session scan A c65decf3d507
carbon-accounting-ghg-protocol is a skill published in the GitHub repository hamzabellouch/agent-skills (8 stars, last pushed today), licensed MIT. It adds 89 tokens to every session and 1,038 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-29.
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