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 itallstartedwithaidea/agent-skills --skill continuous-learninggit clone --depth 1 https://github.com/itallstartedwithaidea/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/itallstartedwithaidea/agent-skills/continuous-learning)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/continuous-learning"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/continuous-learning/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/itallstartedwithaidea/agent-skills/continuous-learning"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/continuous-learning.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 | $0.00030 | $0.02505 |
| Opus 5.5 | $0.00012 | $0.01002 |
| Sonnet 5.5 | $0.00006 | $0.00501 |
| Haiku 4.5 | $0.00003 | $0.00250 |
Grade A, and why
continuous-learning 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 — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Continuous Learning
Part of Agent Skills™ by googleadsagent.ai™
Description
Continuous Learning enables agents to automatically extract successful patterns from completed sessions and codify them into reusable skills, rules, and prompt refinements. Rather than relying on manual skill authoring, a Continuous Learning system treats every agent session as a potential source of new capability. When the agent discovers an effective approach, solves a novel problem, or recovers from an error in a replicable way, the system captures that behavior and integrates it into the agent's skill repertoire.
This skill encodes the learning flywheel built into Buddy™ at googleadsagent.ai™, where cross-session pattern mining has generated dozens of specialized Google Ads analysis techniques that no human engineer explicitly programmed. The system observes which tool sequences produce high-quality outcomes, which prompt modifications improve accuracy, and which error recovery strategies succeed — then packages these observations into structured skills that future sessions can leverage.
The learning pipeline operates in four stages: observation (logging session events with outcome annotations), mining (identifying statistically significant patterns across sessions), validation (testing candidate skills against held-out sessions), and integration (deploying validated skills into the agent's active skill set). This mirrors the scientific method applied to agent behavior: observe, hypothesize, test, deploy.
Use When
- You want agent capabilities to improve automatically over time without manual intervention
- The agent performs repetitive domain-specific tasks where patterns emerge across sessions
- New team members need to benefit from patterns discovered by experienced users
- You need to maintain a living knowledge base that reflects actual best practices
- A/B testing different agent approaches and promoting winners automatically
- Reducing reliance on manual prompt engineering by automating skill derivation
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 · 259 lines · 30 tokens per session scan A 5ea98eeb8ef7
continuous-learning is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (44 stars, last pushed 6mo ago), licensed MIT. It adds 30 tokens to every session and 2,505 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 2026-08-30.
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