continuous-learning

continuous-learning is a skill for Claude Code from hamzaPixl/pixl-ai. It costs 54 tokens per session (744 once invoked), scanned A, original, MIT.

A learning system that notices useful patterns, mistakes, and preferences across coding sessions and stores them as reusable instincts.

In plain words
What is it for?
Use it to review a session, record a specific lesson, or inspect previously saved instincts.
Why use it?
It helps an agent remember lessons between sessions instead of repeating the same mistakes or relying on the user to restate preferences.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the pixl-crew plugin — 93 skills, 14 agents, 6 hooks shipped together

Good fit Use it to review a session, record a specific lesson, or inspect previously saved instincts.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hamzapixl/pixl-ai/continuous-learning
Install

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.

Any agent
npx skills add hamzaPixl/pixl-ai --skill continuous-learning
Clone the repo
git clone --depth 1 https://github.com/hamzaPixl/pixl-ai

Made for: Claude Code.

Or install pixl-crew, the plugin that ships this one along with the rest of its 93 skills, 14 agents, 6 hooks.

Wrote 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.

agentmods badge for continuous-learning

README.md
[![agentmods](https://agentmods.dev/badge/skills/hamzapixl/pixl-ai/continuous-learning.svg)](https://agentmods.dev/skills/hamzapixl/pixl-ai/continuous-learning)
Your own site
<a href="https://agentmods.dev/skills/hamzapixl/pixl-ai/continuous-learning"><img src="https://agentmods.dev/badge/skills/hamzapixl/pixl-ai/continuous-learning.svg" alt="Measured on agentmods" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 744 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00054 $0.00744
Opus 5 $0.00027 $0.00372
Sonnet 5 $0.00011 $0.00149
Haiku 4.5 $0.00005 $0.00074

Measured 7d ago against content hash 30a0bcc985b2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

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 7d 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.

packages/crew/skills/continuous-learning/SKILL.md · 90 lines

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.

Continuous Learning

Pattern-based learning that persists across sessions via .claude/memory/instincts.jsonl.

Actions

observe — Analyze Current Session

  1. Review the conversation for:

    • Mistakes made and corrected
    • Patterns that worked well
    • User corrections or preferences
    • Repeated actions that could be automated
  2. For each observation, draft an instinct:

{
  "timestamp": "2026-03-08T14:30:00Z",
  "trigger": "when editing React components with state",
  "instinct": "always check if the component needs useCallback for handlers passed to children",
  "confidence": 0.7,
  "source": "user corrected missing useCallback in OrderList component",
  "category": "react"
}
  1. Present observations to user for confirmation before recording.

record — Save a Specific Instinct

Accept a description and create an instinct entry:

  1. Parse the description into trigger + instinct + category
  2. Set initial confidence to 0.5
  3. Append to .claude/memory/instincts.jsonl
  4. If pixl is available, also persist as a pixl artifact:
    pixl artifact put --name "instinct-<category>-$(date +%s)" --type instinct --content '<json>'
    

review — Review and Prune Instincts

  1. Read all instincts from .claude/memory/instincts.jsonl
  2. Group by category
  3. Identify:
    • Contradicting instincts (flag for resolution)
    • Low-confidence instincts (< 0.3, suggest removal)
    • Redundant instincts (merge)
    • Stale instincts (not applied in 30+ days)
  4. Present summary and recommendations

apply — Load Relevant Instincts

  1. Read .claude/memory/instincts.jsonl
  2. Filter instincts relevant to the current task (by category, trigger keywords)
  3. Boost confidence for instincts that match (+0.1)
  4. Present applicable instincts as reminders

Instinct Schema

{"timestamp":"...","trigger":"when...","instinct":"always/never/prefer...","confidence":0.5,"source":"...","category":"...","last_applied":"..."}

Categories

Read the full file on GitHub · 90 lines

Changes

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.

  1. 7d ago First seen · 90 lines · 54 tokens per session scan A 30a0bcc985b2

Subscribe to this mod's changes

continuous-learning is a skill published in the GitHub repository hamzaPixl/pixl-ai (2 stars, last pushed 4mo ago), licensed MIT. It adds 54 tokens to every session and 744 once invoked, about $0.0003 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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