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 hsusul/lore --skill lore-lessonsgit clone --depth 1 https://github.com/hsusul/loreWrote 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/hsusul/lore/lore-lessons)<a href="https://agentmods.dev/skills/hsusul/lore/lore-lessons"><img src="https://agentmods.dev/badge/skills/hsusul/lore/lore-lessons/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/hsusul/lore/lore-lessons"><img src="https://agentmods.dev/badge/skills/hsusul/lore/lore-lessons.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 | $0.00044 | $0.01141 |
| Opus 5.5 | $0.00018 | $0.00456 |
| Sonnet 5.5 | $0.00009 | $0.00228 |
| Haiku 4.5 | $0.00004 | $0.00114 |
Grade A, and why
lore-lessons 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 yesterday.
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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lore lessons
You turn this person's past corrections into short rules they can review. The tools come from Lore's MCP server. Each rule you file lands in Lore's inbox as a proposal. The person accepts or rejects it later.
1. Learn what already exists
Call recall_memory with cwd set to the absolute path of the current directory. Leave out query. Read every note it returns. Never propose a rule that an existing note already states. If a rule sharpens an existing note, pass that note's id as updates.
2. Read every candidate
Call mine_candidates with cwd set to the absolute path of the current directory. While its next_cursor is not null, call it again with the same cwd and cursor set to that value. Read every page before you decide anything. If there are no candidates, say so and stop.
Each candidate has the person's words, a suggested rule, receipts, and may_conflict_with. A receipt names the session, agent, date, and repository. It quotes the person's correction, what the agent had just done as before, and the agent's reply as after. The reply's cwd_repo is the repository at the current directory.
3. Understand each candidate
A correction alone is often unclear. "no, cause you never get to access it" means nothing without the turn it answers. For every receipt, read before, then correction, then after, and work out two things.
- What the agent did that the person did not want.
- What the person wanted instead.
If before and after do not make both clear, drop the candidate. Do not guess.
4. Keep standing rules only
Keep a candidate only when it states a rule a future session should follow. Drop these.
- Questions.
- One-off orders about a specific branch, PR, file, or task.
- Explanations of what happened.
- Venting.
5. Group by meaning
Put candidates that mean the same thing in one group, even when the words differ or have typos. One group becomes one rule. The group keeps all its receipts.
6. Check later turns
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.
- yesterday Changed · +12 lines 024e5af7056e
- 3d ago First seen · 80 lines · 44 tokens per session scan A c011b15a5f05
lore-lessons is a skill published in the GitHub repository hsusul/lore (134 stars, last pushed yesterday), licensed Apache-2.0. It adds 44 tokens to every session and 1,141 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 2026-11-04.
lore-lessons compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| lore-lessonshsusul/lore | Skill | 134 | yesterday | Apache-2.0 | A | 44 | Claude Code |
| self-improve-with-tinyfishtinyfish-io/tinyfish-cookbook | Skill | 2.3k | 3d ago | MIT | A | 68 | Claude Code, Codex |
| flow-next-auditgmickel/flow-next | Skill | 717 | yesterday | MIT | A | 46 | Claude Code, Codex |
| learn-from-fixoliver-kriska/claude-elixir-phoenix | Skill | 574 | yesterday | MIT | A | 46 | Claude Code |
| mindosGeminiLight/MindOS | Skill | 704 | 14d ago | MIT | A | 136 | Claude Code |
| openclaw-knowledge-coachSibo-Zhao/OpenPraxis | Skill | 415 | 16d ago | MIT | A | 65 | Codex |
| flashcardsfuyuxiang/echo-agent | Skill | 1.0k | yesterday | none | A | 25 | Claude Code, Codex |