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 TrueClicks/claude-plugins --skill skill-041-smart-bidding-learning-phase-monitoringgit clone --depth 1 https://github.com/TrueClicks/claude-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/skills/trueclicks/claude-plugins/skill-041-smart-bidding-learning-phase-monitoring)<a href="https://agentmods.dev/skills/trueclicks/claude-plugins/skill-041-smart-bidding-learning-phase-monitoring"><img src="https://agentmods.dev/badge/skills/trueclicks/claude-plugins/skill-041-smart-bidding-learning-phase-monitoring/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/trueclicks/claude-plugins/skill-041-smart-bidding-learning-phase-monitoring"><img src="https://agentmods.dev/badge/skills/trueclicks/claude-plugins/skill-041-smart-bidding-learning-phase-monitoring.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 | $0.00721 |
| Opus 5 | $0.00015 | $0.00360 |
| Sonnet 5 | $0.00006 | $0.00144 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
skill-041-smart-bidding-learning-phase-monitoring 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 12d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill 041: Smart Bidding Learning Phase Monitoring
Purpose
Smart Bidding algorithms need time to learn optimal bid patterns. During learning, performance may be volatile, making changes extends the learning period, and evaluation of strategy effectiveness is unreliable. This skill identifies campaigns in learning phase, assesses expected exit timing, and flags disruptions that may extend learning.
Data Requirements
Data Source: Custom GAQL Required
The standard export does not include campaign primary status or learning phase indicators.
Standard Data:
data/account/campaigns/*/campaign.md- Bidding strategy type, statusdata/performance/campaigns/*/campaign_metrics_30_days.md- Daily performance trends
GAQL Query:
SELECT
campaign.id,
campaign.name,
campaign.bidding_strategy_type,
campaign.primary_status,
campaign.primary_status_reasons,
bidding_strategy.status,
bidding_strategy.type,
metrics.conversions,
metrics.cost_micros,
metrics.impressions,
segments.date
FROM campaign
WHERE campaign.status = 'ENABLED'
AND segments.date DURING LAST_14_DAYS
Run via /google-ads:get-custom with query name learning_phase_status.
Analysis Steps
-
Identify Smart Bidding campaigns: Filter for Target CPA, Target ROAS, Maximize Conversions, Maximize Conversion Value strategies.
-
Check learning phase status: Look for primary status = "LEARNING" or learning-related status reasons; identify recent strategy/setting changes (<14 days).
-
Assess learning duration: Track days since strategy change, expected remaining learning time (typically 7-14 days for new strategies).
-
Identify learning disruptions: Flag frequent target adjustments, budget changes, pausing/enabling, or conversion action changes during learning.
-
Evaluate performance volatility: Compare CPA/ROAS variance during learning vs stable periods; check budget utilization.
Thresholds
| Condition | Severity |
|---|---|
| Learning phase >21 days | Critical |
| Multiple changes during active learning | Critical |
| 3+ strategy changes in 30 days | Critical |
| Learning phase >14 days + low volume (<15 conv/month) | High |
| <15 conversions/month during learning | High |
| CPA variance >50% during learning | Warning |
| Budget underspend >40% during learning | Warning |
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.
- 12d ago First seen · 90 lines · 29 tokens per session scan A 805c6fd23ffb
skill-041-smart-bidding-learning-phase-monitoring is a skill published in the GitHub repository TrueClicks/claude-plugins (2 stars, last pushed 2mo ago), licensed MIT. It adds 29 tokens to every session and 721 once invoked, about $0.0001 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 2026-08-31.
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