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/codewithmukesh/dotnet-claude-kit/instinct-systemnpx skills add codewithmukesh/dotnet-claude-kit --skill instinct-systemgit clone --depth 1 https://github.com/codewithmukesh/dotnet-claude-kitWrote 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/codewithmukesh/dotnet-claude-kit/instinct-system)<a href="https://agentmods.dev/skills/codewithmukesh/dotnet-claude-kit/instinct-system"><img src="https://agentmods.dev/badge/skills/codewithmukesh/dotnet-claude-kit/instinct-system.svg" alt="Measured on agentmods" 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 | $0.00202 | $0.03272 |
| Opus 5 | $0.00101 | $0.01636 |
| Sonnet 5 | $0.00040 | $0.00654 |
| Haiku 4.5 | $0.00020 | $0.00327 |
Grade A, and why
instinct-system 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 3d 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instinct System
One learning system, three tiers. Every signal Claude receives during work — an observed pattern, a user correction, a surprising discovery — routes to exactly one store.
Core Principles
-
Three tiers, one routing decision — Instincts are unconfirmed hypotheses (
.claude/instincts.md). Corrections are user-confirmed rules (MEMORY.md). Discoveries are insights that explain the world (.claude/learning-log.md). Rules prescribe behavior; insights describe it; instincts are rules-in-waiting. Never mix the tiers — a hypothesis in MEMORY.md pollutes permanent knowledge, and a confirmed rule left as an instinct gets forgotten. -
Instincts are hypotheses, not rules — An instinct starts as a guess from a single observation and has no authority until confirmed. "One handler uses
sealed" is an instinct at 0.3; "all 12 handlers usesealed" is a rule at 0.9. Confidence (0.3–0.9) drives behavior: note at 0.3, mention at 0.5, follow at 0.7, promote at 0.9. -
A user correction is a confirmed instinct at full confidence — When the user corrects you, skip the confirmation cycle entirely. Generalize the lesson, capture it immediately in MEMORY.md with the "why", and confirm what was captured. A correction costs the user 30 seconds today and saves hours across all future sessions — losing one is the most expensive mistake this system can make.
-
Project-scoped, never global — What holds in one codebase may be wrong in another. Instincts live per-project; transfers between projects go through export/import with confidence decay, never at full confidence.
-
Review at session start, prune periodically — Read MEMORY.md and load instincts at 0.7+ before writing any code; scan recent log entries for the working area. Knowledge captured but never reviewed is wasted effort. Audit all three stores when they bloat — stale instincts, duplicate rules, and unreviewed logs defeat the purpose.
Patterns
Tier Routing
| Signal | Destination | Lifespan |
|---|---|---|
| Pattern observed, not yet confirmed ("I think they always...") | .claude/instincts.md at 0.3 |
Until promoted or discarded |
| User correction ("no, use X", "don't do that again", "remember this") | MEMORY.md immediately, generalized |
Permanent until proven wrong |
| Instinct reaching 0.9 confidence | Promote to MEMORY.md, remove from instincts |
Permanent |
| Non-obvious discovery (bug root cause, gotcha, workaround, perf finding) | .claude/learning-log.md |
3–6 months, then archive or promote |
| Same gotcha logged 3+ times | Promote to MEMORY.md as a preventive rule |
Permanent |
| Session state (done/pending) | Handoff via wrap-up — not this system | Until next session |
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.
- 3d ago First seen · 273 lines · 202 tokens per session scan A cd8638556ff7
instinct-system is a skill published in the GitHub repository codewithmukesh/dotnet-claude-kit (693 stars, last pushed 27d ago), licensed MIT. It adds 202 tokens to every session and 3,272 once invoked, about $0.0010 per session on Opus 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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