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 VersoXBT/claude-initial-setup --skill knowledge-persistencegit clone --depth 1 https://github.com/VersoXBT/claude-initial-setupWrote 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/versoxbt/claude-initial-setup/knowledge-persistence)<a href="https://agentmods.dev/skills/versoxbt/claude-initial-setup/knowledge-persistence"><img src="https://agentmods.dev/badge/skills/versoxbt/claude-initial-setup/knowledge-persistence/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/versoxbt/claude-initial-setup/knowledge-persistence"><img src="https://agentmods.dev/badge/skills/versoxbt/claude-initial-setup/knowledge-persistence.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 | $0.00048 | $0.01427 |
| Opus 5.5 | $0.00019 | $0.00571 |
| Sonnet 5.5 | $0.00010 | $0.00285 |
| Haiku 4.5 | $0.00005 | $0.00143 |
Grade A, and why
knowledge-persistence 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Persistence
Knowledge persistence is about capturing what you learn during sessions so future sessions start smarter. This includes corrections, discovered patterns, user preferences, and debugging insights.
When to Use
- User corrects a mistake originating from stored memory
- You discover a pattern that applies broadly to the project
- A debugging session reveals a non-obvious root cause
- User states a preference ("always do X", "never use Y")
- You solve a problem that is likely to recur
Core Patterns
Learning from Corrections
When the user corrects you, the correction invalidates stored knowledge. Fix the source immediately:
User: "We don't use Express anymore, we migrated to Fastify"
# WRONG: Just acknowledge and continue
"Got it, I'll use Fastify."
# RIGHT: Update memory, then continue
1. Edit memory/MEMORY.md:
Old: "Framework: Express"
New: "Framework: Fastify (migrated from Express)"
2. Check topic files for stale Express references:
Edit memory/architecture.md if it mentions Express
3. Then continue with correct context
The correction means the stored memory is wrong. Fix it at the source before continuing, so the same mistake does not repeat.
Instinct Capture
When you notice yourself repeatedly making the same decision, capture it:
# memory/patterns.md
## Project-Specific Instincts
### Error Handling
- This project always wraps service calls in try/catch at the route level
- Errors are logged with structuredLogger.error(), not console.error()
- All error responses use the AppError class from src/errors.ts
### Naming
- Database functions: findX, createX, updateX, deleteX (not getX, setX)
- Route handlers: handleGetUser, handleCreateUser (not getUser, createUser)
- Test files: same name with .test.ts suffix, same directory
Pattern Extraction
After seeing a pattern in 3+ locations, extract it to memory:
# Observation across multiple files:
# - src/routes/users.ts uses Zod schema + validate middleware
# - src/routes/posts.ts uses Zod schema + validate middleware
# - src/routes/comments.ts uses Zod schema + validate middleware
# Extract to memory/patterns.md:
## Input Validation Pattern
All route handlers use Zod schemas for request validation:
1. Schema defined in src/schemas/{resource}.ts
2. validate(schema) middleware applied to route
3. Handler receives typed, validated body via req.validated
Example:
const schema = z.object({ title: z.string().min(1) })
router.post('/', validate(schema), handleCreatePost)
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 · 185 lines · 48 tokens per session scan A 2bac802604c6
knowledge-persistence is a skill published in the GitHub repository VersoXBT/claude-initial-setup (4 stars, last pushed 5mo ago), licensed MIT. It adds 48 tokens to every session and 1,427 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 2026-09-03.
Other skills, from other repositories
learner
Post-debugging knowledge extraction — captures non-obvious, codebase-specific learnings that pass quality gates. Invoke after resolving tricky bugs or discovering surprising behavior.
principle-guard-the-context-window
Apply when context is filling up: large outputs, long files, repeated reads, fan-out planning. Route bulk to subagents; keep summaries in the main thread, not raw payloads.
agents-skills-feedback-loop
Adds per-skill learnings loops for dated patterns, mistakes, and domain facts. Use when wiring skill memory, consolidation, or drift audits.
dev-context-multi-repo
Builds multi-repo context hubs and compiled markdown knowledge maps. Use when profiling repo portfolios or assembling LLM-ready cross-repo knowledge bases.
memory-search
Search this agent's long-term memory of past sessions: decisions, facts, corrections, and what depends on an entity. Use when a task touches something with history (a repo, service, environment or decision from earlier sessions), before deploying, moving, deleting or reconfiguring something to see what depends on it…
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