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 hamzaPixl/pixl-ai --skill continuous-learninggit clone --depth 1 https://github.com/hamzaPixl/pixl-aiWrote 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/hamzapixl/pixl-ai/continuous-learning)<a href="https://agentmods.dev/skills/hamzapixl/pixl-ai/continuous-learning"><img src="https://agentmods.dev/badge/skills/hamzapixl/pixl-ai/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/hamzapixl/pixl-ai/continuous-learning"><img src="https://agentmods.dev/badge/skills/hamzapixl/pixl-ai/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 | $0.00054 | $0.00744 |
| Opus 5.5 | $0.00022 | $0.00298 |
| Sonnet 5.5 | $0.00011 | $0.00149 |
| Haiku 4.5 | $0.00005 | $0.00074 |
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 — 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
-
Review the conversation for:
- Mistakes made and corrected
- Patterns that worked well
- User corrections or preferences
- Repeated actions that could be automated
-
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"
}
- Present observations to user for confirmation before recording.
record — Save a Specific Instinct
Accept a description and create an instinct entry:
- Parse the description into trigger + instinct + category
- Set initial confidence to 0.5
- Append to
.claude/memory/instincts.jsonl - 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
- Read all instincts from
.claude/memory/instincts.jsonl - Group by category
- Identify:
- Contradicting instincts (flag for resolution)
- Low-confidence instincts (< 0.3, suggest removal)
- Redundant instincts (merge)
- Stale instincts (not applied in 30+ days)
- Present summary and recommendations
apply — Load Relevant Instincts
- Read
.claude/memory/instincts.jsonl - Filter instincts relevant to the current task (by category, trigger keywords)
- Boost confidence for instincts that match (+0.1)
- Present applicable instincts as reminders
Instinct Schema
{"timestamp":"...","trigger":"when...","instinct":"always/never/prefer...","confidence":0.5,"source":"...","category":"...","last_applied":"..."}
Categories
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 · 90 lines · 54 tokens per session scan A 30a0bcc985b2
continuous-learning is a skill published in the GitHub repository hamzaPixl/pixl-ai (2 stars, last pushed 6mo ago), licensed MIT. It adds 54 tokens to every session and 744 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
knowledge-ingest
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forget
Remove a personal fact, preference, relationship detail, topic, or prior event from Muse's active memory and stop existing copies or automations from bringing it back. Use for explicit requests such as 'forget that', 'don't remember this about me', or 'remove that from your memory'. Do not use when 'forget it' merely…
mem0-oss-to-platform
Plan, then execute, a migration of a project from the mem0 open-source / self-hosted SDK (local Memory class) to the mem0 Platform / hosted SDK (MemoryClient). TRIGGER when: the developer wants to move, switch, or migrate mem0 off OSS/self-hosted to the hosted API, e.g. "migrate my mem0 setup to the platform", "switch…
Cortex
LifeOS memory, prior-session recall, and learning capture. USE WHEN capture this learning, document this learning, what did we learn recently, recent learnings, cortex, knowledge, knowledge base, search knowledge, what do we know about, archive, harvest, knowledge status, develop note, add to knowledge, add a people…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…