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 Snowflake-Labs/cocoplus --skill meter-comparegit clone --depth 1 https://github.com/Snowflake-Labs/cocoplusWrote 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/snowflake-labs/cocoplus/meter-compare)<a href="https://agentmods.dev/skills/snowflake-labs/cocoplus/meter-compare"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/meter-compare/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/snowflake-labs/cocoplus/meter-compare"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/meter-compare.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 | $0.00029 | $0.00931 |
| Opus 5.5 | $0.00012 | $0.00372 |
| Sonnet 5.5 | $0.00006 | $0.00186 |
| Haiku 4.5 | $0.00003 | $0.00093 |
Grade A, and why
meter-compare 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 1mo 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use this skill for $meter compare.
Objective
Compare two CocoMeter or harness benchmark result files while respecting the configured optimization objective. The default is correctness-first: accuracy delta is the primary signal and token/cost delta is secondary.
CocoMeter session summaries may include three named cost rows:
execution_cost: functional stage work.coordination_cost: advisor calls, handoff context assembly, artifact transfer, and status sync.landing_cost: reserve-window spend for final evaluation, console update, and handoff.
When [meter] track_acrr = true, session summaries may also include:
acrr_this_session: average Agent Cognitive Redundancy Ratio across completed CocoFlow runs in the session.acrr_runs[]: the last run-level records withcomplexity_tier_estimated,model_tier_configured,model_tier_actual,model_tier_used,model_drift,escalations_taken, andacrr.
When [meter] meter_reconciliation_enabled = true, finalized session summaries may include reconciliation_status, metering_gap_fraction, duplicates_found, model_tier_configured, model_tier_actual, and model_drift. Treat transcript-derived totals as authoritative for billing comparisons when reconciliation_status is gap_corrected.
Treat ACRR as a calibration diagnostic, not an accuracy score. ACRR near 1.0 indicates that the initial complexity tier was sufficient. Consistently high ACRR indicates the task class is being underestimated before dispatch.
Workflow
- Parse
$meter compare <before.json> <after.json>. - Read both files. Accept fields named
accuracy,score,task_accuracy,cost,tokens, orcredits. - Read
cocoplus.toml [meter]:optimization_objective, defaultcorrectness-firstaccuracy_equivalence_band, default0.02cost_first_acknowledged, defaultfalsetrack_acrr, defaulttrue
- Compute accuracy delta before cost delta.
- If objective is
cost-firstbutcost_first_acknowledgedis not true, treat the run as correctness-first and warn. - If both files include ACRR, report ACRR trend after accuracy and before detailed cost rows. A lower ACRR with unchanged accuracy means the harness is better calibrated; a higher ACRR with unchanged accuracy means the run succeeded but over-expanded.
- If either file includes reconciliation or model-drift metadata, report it after ACRR and before cost rows.
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.
- 1mo ago First seen · 81 lines · 29 tokens per session scan A b989969b17ee
meter-compare is a skill published in the GitHub repository Snowflake-Labs/cocoplus (723 stars, last pushed 18d ago), licensed MIT. It adds 29 tokens to every session and 931 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 2026-09-03.
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