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 agentmods add skills/magic3007/dotfiles/claudeceptionnpx skills add magic3007/dotfiles --skill claudeceptiongit clone --depth 1 https://github.com/magic3007/dotfilesWhat 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 | $0.00099 | $0.03281 |
| Opus 5 | $0.00049 | $0.01640 |
| Sonnet 5 | $0.00020 | $0.00656 |
| Haiku 4.5 | $0.00010 | $0.00328 |
Grade A, and why
claudeception 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 2d 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.
This is a copy
92% identical to continuous-learning — 65 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 390 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claudeception
You are Claudeception: 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: Check for Existing Skills
Goal: Find related skills before creating. Decide: update or create new.
# Skill directories (project-first, then user-level)
SKILL_DIRS=(
".claude/skills"
"$HOME/.claude/skills"
"$HOME/.codex/skills"
# Add other tool paths as needed
)
# List all skills
rg --files -g 'SKILL.md' "${SKILL_DIRS[@]}" 2>/dev/null
# Search by keywords
rg -i "keyword1|keyword2" "${SKILL_DIRS[@]}" 2>/dev/null
# Search by exact error message
rg -F "exact error message" "${SKILL_DIRS[@]}" 2>/dev/null
# Search by context markers (files, functions, config keys)
rg -i "getServerSideProps|next.config.js|prisma.schema" "${SKILL_DIRS[@]}" 2>/dev/null
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/nextjs-server-side-error-debugging/SKILL.md 4.0 KB
- examples/prisma-connection-pool-exhaustion/SKILL.md 4.6 KB
- examples/typescript-circular-dependency/SKILL.md 5.6 KB
- LICENSE 1.0 KB
- README.md 6.0 KB
- resources/research-references.md 7.5 KB
- resources/skill-template.md 2.1 KB
- scripts/claudeception-activator.sh 1.7 KB runs code
- test-skill-parsing.yaml 184 B
- WARP.md 1.7 KB
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.
- 2d ago First seen · 390 lines · 99 tokens per session scan A a2b636da2fc3
claudeception is a skill published in the GitHub repository magic3007/dotfiles (10 stars, last pushed 6d ago), licensed MIT. It adds 99 tokens to every session and 3,281 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to continuous-learning, differing in 65 lines, and is treated as a copy.
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test-code
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