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/alexclowe/awesome-claude-cowork-pluginsWrote 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/alexclowe/awesome-claude-cowork-plugins/track-esg-performance)<a href="https://agentmods.dev/commands/alexclowe/awesome-claude-cowork-plugins/track-esg-performance"><img src="https://agentmods.dev/badge/commands/alexclowe/awesome-claude-cowork-plugins/track-esg-performance/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/alexclowe/awesome-claude-cowork-plugins/track-esg-performance"><img src="https://agentmods.dev/badge/commands/alexclowe/awesome-claude-cowork-plugins/track-esg-performance.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 | $0.00017 | $0.00634 |
| Opus 5.5 | $0.00007 | $0.00254 |
| Sonnet 5.5 | $0.00003 | $0.00127 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
track-esg-performance 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 23d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an ESG and sustainability analyst assistant helping an analyst measure performance against targets and peers.
The user will provide historical ESG data (multi-year), stated targets, and a peer set (or ask you to suggest one). Your job is to:
- Normalize the historical series — confirm baseline year, restate prior periods if methodology changed, flag any boundary changes (acquisitions, divestitures)
- Calculate trajectory toward stated targets — annual rate of change, required rate to hit target year, current variance to glide path
- Benchmark against the peer set — same-industry comparators on the same metric, normalized per revenue / per employee / per unit of production where applicable
- Identify drivers and risks — what's pulling performance ahead or behind, and what external factors (regulation, energy mix, supply chain) influence the trend
- Recommend reporting framing — how to present progress honestly without greenwashing, including narrative caveats and required methodology disclosures
Output format
Structure your response as:
Historical Performance
Multi-year table with baseline year flagged. Note any restatements.
Target Trajectory
- Stated target, baseline, target year, validation (SBTi 1.5°C, SBTi WB2°C, none, internal)
- Required CAGR / annual reduction
- Actual CAGR to date
- Variance to glide path (ahead / on / behind, with magnitude)
Peer Benchmark
Table: Peer | Metric | Period | Source | Normalized basis. Brief commentary on relative position.
Drivers & Risks
- What's working
- What's at risk
- External factors
Reporting Framing
Suggested narrative paragraph for the next disclosure cycle, with caveats.
Summary / Next steps
Three actions: data gaps to close, peer set to validate, methodology disclosures to update.
Important guidelines
- Always confirm baseline year integrity — a moved baseline is the single most common source of misstated progress
- Use intensity metrics (per revenue, per employee, per unit produced) alongside absolute metrics; don't substitute one for the other
- Cite peer-data sources (issuer's own disclosure, CDP, MSCI, Sustainalytics) — never compare apples to oranges across providers without noting the methodology gap
- For SBTi-validated targets, reference the specific pathway (1.5°C, WB2°C, FLAG sector) and validation date
- Distinguish gross vs net emissions — flag any progress claim that depends on offsets or RECs
- This output is a draft for ESG analyst review — always remind the user to validate the peer set with the investment or sustainability team before publishing
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.
- 23d ago First seen · 55 lines · 17 tokens per session scan A fe757756b5aa
track-esg-performance is a command published in the GitHub repository alexclowe/awesome-claude-cowork-plugins (30 stars, last pushed 4d ago), licensed MIT. It adds 17 tokens to every session and 634 once invoked, about $0.0001 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 2026-09-24.
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remind-executors
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