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 cloud99277/KitClaw --skill continuous-learning-v2git clone --depth 1 https://github.com/cloud99277/KitClawWrote 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/cloud99277/kitclaw/continuous-learning-v2)<a href="https://agentmods.dev/skills/cloud99277/kitclaw/continuous-learning-v2"><img src="https://agentmods.dev/badge/skills/cloud99277/kitclaw/continuous-learning-v2/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/cloud99277/kitclaw/continuous-learning-v2"><img src="https://agentmods.dev/badge/skills/cloud99277/kitclaw/continuous-learning-v2.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 | $0.00052 | $0.02096 |
| Opus 5.5 | $0.00021 | $0.00838 |
| Sonnet 5.5 | $0.00010 | $0.00419 |
| Haiku 4.5 | $0.00005 | $0.00210 |
Grade B, and why
continuous-learning-v2 scanned grade B with 1 finding 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
Add to your `~/.claude/settings.json`. This is a copy
91% identical to continuous-learning-v2 — 10 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 — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Continuous Learning v2 - Instinct-Based Architecture
An advanced learning system that turns your Claude Code sessions into reusable knowledge through atomic "instincts" - small learned behaviors with confidence scoring.
Inspired in part by the Homunculus work from humanplane.
When to Activate
- Setting up automatic learning from Claude Code sessions
- Configuring instinct-based behavior extraction via hooks
- Tuning confidence thresholds for learned behaviors
- Reviewing, exporting, or importing instinct libraries
- Evolving instincts into full skills, commands, or agents
What's New in v2
| Feature | v1 | v2 |
|---|---|---|
| Observation | Stop hook (session end) | PreToolUse/PostToolUse (100% reliable) |
| Analysis | Main context | Background agent (Haiku) |
| Granularity | Full skills | Atomic "instincts" |
| Confidence | None | 0.3-0.9 weighted |
| Evolution | Direct to skill | Instincts → cluster → skill/command/agent |
| Sharing | None | Export/import instincts |
The Instinct Model
An instinct is a small learned behavior:
---
id: prefer-functional-style
trigger: "when writing new functions"
confidence: 0.7
domain: "code-style"
source: "session-observation"
---
# Prefer Functional Style
## Action
Use functional patterns over classes when appropriate.
## Evidence
- Observed 5 instances of functional pattern preference
- User corrected class-based approach to functional on 2025-01-15
Properties:
- Atomic — one trigger, one action
- Confidence-weighted — 0.3 = tentative, 0.9 = near certain
- Domain-tagged — code-style, testing, git, debugging, workflow, etc.
- Evidence-backed — tracks what observations created it
How It Works
Session Activity
│
│ Hooks capture prompts + tool use (100% reliable)
▼
┌─────────────────────────────────────────┐
│ observations.jsonl │
│ (prompts, tool calls, outcomes) │
└─────────────────────────────────────────┘
│
│ Observer agent reads (background, Haiku)
▼
┌─────────────────────────────────────────┐
│ PATTERN DETECTION │
│ • User corrections → instinct │
│ • Error resolutions → instinct │
│ • Repeated workflows → instinct │
└─────────────────────────────────────────┘
│
│ Creates/updates
▼
┌─────────────────────────────────────────┐
│ instincts/personal/ │
│ • prefer-functional.md (0.7) │
│ • always-test-first.md (0.9) │
│ • use-zod-validation.md (0.6) │
└─────────────────────────────────────────┘
│
│ /evolve clusters
▼
┌─────────────────────────────────────────┐
│ evolved/ │
│ • commands/new-feature.md │
│ • skills/testing-workflow.md │
│ • agents/refactor-specialist.md │
└─────────────────────────────────────────┘
What ships with it
1 file 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.
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 · 294 lines · 52 tokens per session scan B edb54ce0a7b0
continuous-learning-v2 is a skill published in the GitHub repository cloud99277/KitClaw (5 stars, last pushed 6mo ago), licensed MIT. It adds 52 tokens to every session and 2,096 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). It is 91% identical to continuous-learning-v2, differing in 10 lines, and is treated as a copy.
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