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/roberto-mello/lavra/lavra-knowledgenpx skills add roberto-mello/lavra --skill lavra-knowledgegit clone --depth 1 https://github.com/roberto-mello/lavraWrote 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/roberto-mello/lavra/lavra-knowledge)<a href="https://agentmods.dev/skills/roberto-mello/lavra/lavra-knowledge"><img src="https://agentmods.dev/badge/skills/roberto-mello/lavra/lavra-knowledge.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 5.1 | $0.00026 | $0.03631 |
| Opus 5 | $0.00013 | $0.01816 |
| Sonnet 5 | $0.00005 | $0.00726 |
| Haiku 4.5 | $0.00003 | $0.00363 |
Grade A, and why
lavra-knowledge scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
lavra-knowledge Skill
Purpose: Capture solved problems as structured JSONL entries in .lavra/memory/knowledge.jsonl and as bead comments, building a searchable knowledge base that auto-recall injects into future sessions.
Overview
Captures problem solutions immediately after confirmation, creating structured knowledge entries stored in .lavra/memory/knowledge.jsonl for auto-recall search and logged as bead comments for traceability. Uses the five knowledge prefixes: LEARNED, DECISION, FACT, PATTERN, INVESTIGATION.
Organization: Append-only JSONL file. Each solved problem produces one or more entries. The auto-recall hook (auto-recall.sh) searches by keyword and injects relevant entries at session start.
<project_root>
All .lavra/ paths are relative to the project root. If you cd into a subdirectory during work, resolve the project root first:
PROJECT_ROOT=$(git rev-parse --show-toplevel 2>/dev/null || echo "$PWD")
Then prefix all .lavra/ paths with "$PROJECT_ROOT/" when invoking them via Bash.
</project_root>
<critical_sequence name="knowledge-capture" enforce_order="strict">
7-Step Process
Auto-invoke after phrases:
- "that worked"
- "it's fixed"
- "working now"
- "problem solved"
- "that did it"
OR manual invocation.
Non-trivial problems only: multiple investigation attempts, tricky debugging, non-obvious solution, or future sessions would benefit.
Skip for: simple typos, obvious syntax errors, trivial fixes.
Extract from conversation history:
Required:
- Area/module: Which part of the codebase had the problem
- Symptom: Observable error/behavior (exact error messages)
- Investigation attempts: What didn't work and why
- Root cause: Technical explanation of actual problem
- Solution: What fixed it (code/config changes)
- Prevention: How to avoid in future
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.
- 6d ago First seen · 422 lines · 26 tokens per session scan A 87a6e8c6b976
lavra-knowledge is a skill published in the GitHub repository roberto-mello/lavra (50 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 3,631 once invoked, about $0.0001 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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