Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Encod3d-Sec/TORCHnpx agentmods add skills/encod3d-sec/torch/learnWrote 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/encod3d-sec/torch/learn)<a href="https://agentmods.dev/skills/encod3d-sec/torch/learn"><img src="https://agentmods.dev/badge/skills/encod3d-sec/torch/learn/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/encod3d-sec/torch/learn"><img src="https://agentmods.dev/badge/skills/encod3d-sec/torch/learn.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 | $0.00139 | $0.03971 |
| Opus 5.5 | $0.00056 | $0.01588 |
| Sonnet 5.5 | $0.00028 | $0.00794 |
| Haiku 4.5 | $0.00014 | $0.00397 |
Grade A, and why
learn 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.
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn: harness retrospective + wiki knowledge harvest
The close-out retrospective, in two phases:
- Phase 0 (process): diff how the engagement was EXECUTED against the discipline the skills/hooks prescribe, find where the operator (you) or the harness drifted, and fix the harness so the next box does not repeat it. This is the "improve the harness on it" half.
- Phases 1-7 (knowledge): read the WHOLE finished engagement, diff it against the existing wiki, and promote only the DELTA as durable, generic knowledge. The safety net that catches everything not captured live during the box.
The client-data boundary is enforced by code, not by this prose: engagement-derived
content reaches wiki/ ONLY through wiki-stage.py -> wiki-promote.py, which runs
check-leaks.sh and fails closed. This skill READS the engagement and WRITES only
generic knowledge; it never edits the engagement's own files.
When it fires
- At close-out: run
Skill(learn)once the engagement is marked## STATUS: SOLVED(OWNED/ROOTED/COMPLETE) AND itswalkthrough.mdis assembled (the CLAUDE.md execution-loop close-out step). It self-clears the moment this skill writes<eng>/.learn-done. - On demand: "extract learnings", "what did we learn", "harvest lessons", "distill", "post-mortem into the wiki" - the same steps apply.
Phase 0: Process retrospective (harness self-improvement) - DO FIRST
Before harvesting knowledge, ask: did we execute the way the skills/hooks told us to, and where we did not, whose fault is it - mine, or the harness's for not catching me? The point is not self-flagellation; it is to turn each drift into a concrete harness change so the next engagement cannot repeat it.
0a. Diff execution against discipline
Re-read the skills that governed this engagement (ctf-box, the hunt-* used, the CLAUDE.md
execution loop) and check each mandated step against what actually happened on disk:
| Discipline it prescribes | How to check it was (not) done | Common drift |
|---|---|---|
| Recon tooling complete (nmap AND ffuf AND nuclei-read for web) | ls targets/$ENG/recon/*.png, .recon-tools marker, tmux window names, log.md |
ffuf never run; nuclei launched but output never read |
| Screenshot EVERY finding as it lands | count deliberate poc/*.png vs findings in Killchain.md; .screenshot-nudged |
shots only at the very end; transient states lost |
| Wiki-first before exploiting each fingerprinted service | wiki queries in transcript / log.md |
jumped to exploitation from memory |
| A hook nudge fired -> was it acted on? | grep the transcript for a nudge (e.g. "switch to ffuf") whose action never followed | nudge ignored under momentum |
| State-first / capture-as-you-go | state.md/loot.md updated mid-box vs all-at-end |
prose-in-chat lost |
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 · 246 lines · 139 tokens per session scan A 75108f6574dc
learn is a skill published in the GitHub repository Encod3d-Sec/TORCH (336 stars, last pushed 1mo ago), licensed MIT. It adds 139 tokens to every session and 3,971 once invoked, about $0.0006 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.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…