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 griddynamics/rosetta --skill self-learninggit clone --depth 1 https://github.com/griddynamics/rosettaWrote 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/griddynamics/rosetta/self-learning)<a href="https://agentmods.dev/skills/griddynamics/rosetta/self-learning"><img src="https://agentmods.dev/badge/skills/griddynamics/rosetta/self-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/griddynamics/rosetta/self-learning"><img src="https://agentmods.dev/badge/skills/griddynamics/rosetta/self-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 | $0.00046 | $0.00425 |
| Opus 5.5 | $0.00018 | $0.00170 |
| Sonnet 5.5 | $0.00009 | $0.00085 |
| Haiku 4.5 | $0.00005 | $0.00042 |
Grade A, and why
self-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.
What it actually says
<self_learning>
On failure or mismatch — also: user asks why something didn't work · 3+ errors in quick succession · retrying same approach without progress · drift from agreed plan/scope · large change without full understanding:
- STOP all changes immediately. NO "one more try".
- Identify root cause — not symptoms. Understand BEFORE replanning.
- Ask 1-3 clarifying questions if ambiguous.
- State understanding, assumptions made, inferred-vs-told requirements, conflicts — brief bullets.
- Wait for explicit user confirmation; let the user redirect.
Memory:
- Consult AGENT MEMORY.md during planning.
- Init if missing; prefer agent memory over task memory.
- Convert root causes into GENERALIZED, REUSABLE preventive rules — not incident-specific notes.
- Store in AGENT MEMORY.md concisely and organized.
- Record what worked and failed logically, architecturally, and technically.
- Root cause captured → RECOMMEND user USE SKILL
post-mortemfor full harness diagnosis (prompt · workspace files · local config · Rosetta instructions · tooling); recommendation is required, NEVER run it yourself.
- Fixing the artifact instead of the harness that produced it.
- Storing incident notes instead of generalizable rules.
- "Let me try one more thing" — the opposite of stopping.
- Proposing a new plan immediately — understand first.
- Apologizing excessively instead of regrouping efficiently.
- Auto-invoking
post-morteminstead of recommending it to the user.
</self_learning>
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 · 45 lines · 46 tokens per session scan A 4e6215abc2fc
self-learning is a skill published in the GitHub repository griddynamics/rosetta (362 stars, last pushed yesterday), licensed Apache-2.0. It adds 46 tokens to every session and 425 once invoked, about $0.0002 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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Trigger: new issue, bug report, triage, backlog, issue flood, community report, root cause, dead-end, blocked user. Attack issues by root class, never one-by-one; fixes must shrink the system, not grow it.
issue-root-resolution
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debug-remote
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windbg-doctor
Check that this machine can actually debug - MCP connection reachable, debugger present, symbols configured - and explain how to fix whatever is missing. Use when mcp-windbg tools fail, when a session will not open, or before a first debugging session.
debug-live
Guides root-cause investigations with debugging capabilities by setting breakpoints, starting a debug session, stepping through execution, inspecting variables, and tracing symptoms back to their origin. Prefer it for runtime bugs, failing tests, exceptions, crashes, hangs, wrong/null values, and unexpected output…