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 magic3007/dotfiles --skill claudeceptiongit clone --depth 1 https://github.com/magic3007/dotfilesWrote 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/magic3007/dotfiles/claudeception)<a href="https://agentmods.dev/skills/magic3007/dotfiles/claudeception"><img src="https://agentmods.dev/badge/skills/magic3007/dotfiles/claudeception/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/magic3007/dotfiles/claudeception"><img src="https://agentmods.dev/badge/skills/magic3007/dotfiles/claudeception.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 | $0.00117 | $0.03536 |
| Opus 5.5 | $0.00047 | $0.01414 |
| Sonnet 5.5 | $0.00023 | $0.00707 |
| Haiku 4.5 | $0.00012 | $0.00354 |
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 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.
This is a copy
92% identical to continuous-learning — 99 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 — 412 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 for discovery (project-local first; user-level is read-only by default)
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 6.3 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 1.9 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.
- 1mo ago Changed · +22 lines · +18 tokens per session 24d81fe2a2b9
- 1mo ago First seen · 390 lines · 99 tokens per session scan A a2b636da2fc3
claudeception is a skill published in the GitHub repository magic3007/dotfiles (11 stars, last pushed yesterday), licensed MIT. It adds 117 tokens to every session and 3,536 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to continuous-learning, differing in 99 lines, and is treated as a copy.
Other skills, from other repositories
assess-quality
Foundational quality framework: the five questions (readable, easy to start, expands without bloat, consistent, intentional) every other dev skill is judged against, plus the dual-audience and workshop principles. Use when onboarding to a project, defining a quality bar, setting an assessment checklist, or arbitrating…
create-oss-skill
Create well-formed Agent Skills following the agentskills.io specification. Scaffold directories, write SKILL.md files, bundle scripts, and structure instructions for progressive disclosure. Use when creating a new skill, reviewing skill structure, optimizing a skill description, or setting up evals for skill quality.
extend-oss-skills-to-claude
Extend standard agentskills.io skills with Claude Code-specific features. Invocation control, subagent execution, dynamic context injection, string substitutions, model/effort overrides, and deployment scoping. Use when adapting a portable skill for Claude Code, adding Claude-specific frontmatter, setting up subagent…
merge-ready
Drive an existing pull request to a mergeable state: get CI green, resolve merge conflicts with the base branch, address and resolve review comments, trigger required bot reviews/approvals (e.g. commenting '@claude review'), link associated issues, and clean up the PR title and description. Ends with a readiness…
orchestrate-agents
Orchestrate multiple agent CLIs (Claude, Codex, Antigravity) via tmux with a shared fleet store, dispatching one guardian subagent per pane. Survey-first: inspects and adopts existing tmux sessions, windows, and agent panes before creating anything new. Use when running a multi-agent session, dispatching parallel…
scaffold-project
Generates cross-language standard files (README, AGENTS.md, LICENSE, CONTRIBUTING.md, SECURITY.md, sr.yaml, .envrc, llms.txt), documentation conventions, and project structure, then dispatches to language-specific scaffolds. Use first for cross-language standard files and structure, THEN load the matching scaffold…