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 blader/Claudeception --skill continuous-learninggit clone --depth 1 https://github.com/blader/ClaudeceptionWrote 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/blader/claudeception/continuous-learning)<a href="https://agentmods.dev/skills/blader/claudeception/continuous-learning"><img src="https://agentmods.dev/badge/skills/blader/claudeception/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/blader/claudeception/continuous-learning"><img src="https://agentmods.dev/badge/skills/blader/claudeception/continuous-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00093 | $0.02877 |
| Opus 5.5 | $0.00037 | $0.01151 |
| Sonnet 5.5 | $0.00019 | $0.00575 |
| Haiku 4.5 | $0.00009 | $0.00288 |
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.
Copies of this mod
2 near-identical copies found in the catalogue:
- claudeception — 92% identical, 99 lines differ
- claudeception — 92% identical, 65 lines differ
How it starts
The opening of the file, as written. The whole thing — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Continuous Learning Skill
You are a continuous learning system that extracts reusable knowledge from work sessions and codifies it into new Claude Code skills. This enables autonomous improvement over time.
Core Principle: Skill Extraction
When working on tasks, continuously evaluate whether the current work contains extractable knowledge worth preserving. Not every task produces a skill—be selective about what's truly reusable and valuable.
When to Extract a Skill
Extract a skill when you encounter:
-
Non-obvious Solutions: Debugging techniques, workarounds, or solutions that required significant investigation and wouldn't be immediately apparent to someone facing the same problem.
-
Project-Specific Patterns: Conventions, configurations, or architectural decisions specific to this codebase that aren't documented elsewhere.
-
Tool Integration Knowledge: How to properly use a specific tool, library, or API in ways that documentation doesn't cover well.
-
Error Resolution: Specific error messages and their actual root causes/fixes, especially when the error message is misleading.
-
Workflow Optimizations: Multi-step processes that can be streamlined or patterns that make common tasks more efficient.
Skill Quality Criteria
Before extracting, verify the knowledge meets these criteria:
- Reusable: Will this help with future tasks? (Not just this one instance)
- Non-trivial: Is this knowledge that requires discovery, not just documentation lookup?
- Specific: Can you describe the exact trigger conditions and solution?
- Verified: Has this solution actually worked, not just theoretically?
Extraction Process
Step 1: Identify the Knowledge
Analyze what was learned:
- What was the problem or task?
- What was non-obvious about the solution?
- What would someone need to know to solve this faster next time?
- What are the exact trigger conditions (error messages, symptoms, contexts)?
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 · 351 lines · 93 tokens per session scan A 360329e6c37d
continuous-learning is a skill published in the GitHub repository blader/Claudeception (2,434 stars, last pushed 8mo ago), licensed MIT. It adds 93 tokens to every session and 2,877 once invoked, about $0.0004 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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