Continuous-Claude-v3 is a Claude Code development environment that preserves working context between sessions, coordinates specialized agents, and stores project knowledge through ledgers, handoffs, and analysis tools. It is for people using Claude Code on ongoing or complex software work. Its catalogue entries are the skills, agents, hooks, plugin, and setting that provide its workflows and orchestration.
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/parcadei/Continuous-Claude-v3npx agentmods add skills/parcadei/continuous-claude-v3/rememberWrote 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/parcadei/continuous-claude-v3/remember)<a href="https://agentmods.dev/skills/parcadei/continuous-claude-v3/remember"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/remember/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/parcadei/continuous-claude-v3/remember"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/remember.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk fail
- 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 | $0.00017 | $0.00458 |
| Opus 5 | $0.00009 | $0.00229 |
| Sonnet 5 | $0.00003 | $0.00092 |
| Haiku 4.5 | $0.00002 | $0.00046 |
Grade A, and why
remember 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 7d 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
Remember - Store Learning in Memory
Store a learning, pattern, or decision in the memory system for future recall.
Usage
/remember <what you learned>
Or with explicit type:
/remember --type WORKING_SOLUTION <what you learned>
Examples
/remember TypeScript hooks require npm install before they work
/remember --type ARCHITECTURAL_DECISION Session affinity uses terminal PID
/remember --type FAILED_APPROACH Don't use subshell for store_learning command
What It Does
- Stores the learning in PostgreSQL with BGE embeddings
- Auto-detects learning type if not specified
- Extracts tags from content
- Returns confirmation with ID
Learning Types
| Type | Use For |
|---|---|
WORKING_SOLUTION |
Fixes, solutions that worked (default) |
ARCHITECTURAL_DECISION |
Design choices, system structure |
CODEBASE_PATTERN |
Patterns discovered in code |
FAILED_APPROACH |
What didn't work |
ERROR_FIX |
Specific error resolutions |
Execution
When this skill is invoked, run:
cd $CLAUDE_OPC_DIR && PYTHONPATH=. uv run python scripts/core/store_learning.py \
--session-id "manual-$(date +%Y%m%d-%H%M)" \
--type <TYPE or WORKING_SOLUTION> \
--content "<ARGS>" \
--context "manual entry via /remember" \
--confidence medium
Auto-Type Detection
If no --type specified, infer from content:
- Contains "error", "fix", "bug" → ERROR_FIX
- Contains "decided", "chose", "architecture" → ARCHITECTURAL_DECISION
- Contains "pattern", "always", "convention" → CODEBASE_PATTERN
- Contains "failed", "didn't work", "don't" → FAILED_APPROACH
- Default → WORKING_SOLUTION
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.
- 7d ago First seen · 69 lines · 17 tokens per session scan A f1d5abf2fdc8
remember is a skill published in the GitHub repository parcadei/Continuous-Claude-v3 (3,937 stars, last pushed 7mo ago), licensed MIT. It adds 17 tokens to every session and 458 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 2026-09-03.
Other skills, from other repositories
sfl
Save For Later: checkpoint this window so a fresh session (or /nil) can resume it later. Use when the user types /sfl, "save for later", or "checkpoint this window". Writes a live per-window entry under /.claude/sfl/ (plus an optional durable note in the user's own memory system), then shows the green Saved-for-Later…
synthesize
Deep cross-domain synthesis engine for the vault. Triggers on "synthesize X", "what do I know about X", "connect ideas about X", "cross-domain analysis of X", "map everything in the vault about X". Searches comprehensively, surfaces non-obvious patterns and tensions, then challenges the user to generate original…
init
Initialize an Obsidian vault with the agent-ready Zettelkasten system — creates directory structure, templates, CLAUDE.md, MCP config, and hooks.
kb-init
Use to create a new llmkb knowledge base for a project — scaffold it with llmkb init, then interview the user briefly to tailor SCHEMA.md to the project's domain and conventions.
kb-lint
Use to health-check an llmkb knowledge base — run llmkb lint and fix what it reports, then do the judgment-level checks code cannot: contradictions between pages, stale claims superseded by newer sources, and missing concept pages.
vault-graph
Use when analyzing vault structure, finding orphan notes, discovering missing connections, identifying bridge concepts, or checking vault health from a graph perspective. Triggers on "vault graph", "map vault", "find orphans", "missing links", "vault structure", "knowledge graph".