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/recall-reasoningWrote 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/recall-reasoning)<a href="https://agentmods.dev/skills/parcadei/continuous-claude-v3/recall-reasoning"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/recall-reasoning/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/recall-reasoning"><img src="https://agentmods.dev/badge/skills/parcadei/continuous-claude-v3/recall-reasoning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00014 | $0.00717 |
| Opus 5 | $0.00007 | $0.00358 |
| Sonnet 5 | $0.00003 | $0.00143 |
| Haiku 4.5 | $0.00001 | $0.00072 |
Grade A, and why
recall-reasoning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recall Past Work
Search through previous sessions to find relevant decisions, approaches that worked, and approaches that failed. Queries two sources:
- Artifact Index - Handoffs, plans, ledgers with post-mortems (what worked/failed)
- Reasoning Files - Build attempts, test failures, commit context
When to Use
- Starting work similar to past sessions
- "What did we do last time with X?"
- Looking for patterns that worked before
- Investigating why something was done a certain way
- Debugging an issue encountered previously
Usage
Primary: Artifact Index (rich context)
uv run python scripts/core/artifact_query.py "<query>" [--outcome SUCCEEDED|FAILED] [--limit N]
This searches handoffs with post-mortems (what worked, what failed, key decisions).
Secondary: Reasoning Files (build attempts)
bash "$CLAUDE_PROJECT_DIR/.claude/scripts/search-reasoning.sh" "<query>"
This searches .git/claude/commits/*/reasoning.md for build failures and fixes.
Examples
# Search for authentication-related work
uv run python scripts/core/artifact_query.py "authentication OAuth JWT"
# Find only successful approaches
uv run python scripts/core/artifact_query.py "implement agent" --outcome SUCCEEDED
# Find what failed (to avoid repeating mistakes)
uv run python scripts/core/artifact_query.py "hook implementation" --outcome FAILED
# Search build/test reasoning
bash "$CLAUDE_PROJECT_DIR/.claude/scripts/search-reasoning.sh" "TypeError"
What Gets Searched
Artifact Index (handoffs, plans, ledgers):
- Task summaries and status
- What worked - Successful approaches
- What failed - Dead ends and why
- Key decisions - Choices with rationale
- Goal and constraints from ledgers
Reasoning Files (.git/claude/):
- Failed build attempts and error output
- Successful builds after failures
- Commit context and branch info
Interpreting Results
From Artifact Index:
✓= SUCCEEDED outcome (pattern to follow)✗= FAILED outcome (pattern to avoid)?= UNKNOWN outcome (not yet marked)- Post-mortem sections show distilled learnings
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 · 99 lines · 14 tokens per session scan A 6ee9a393b626
recall-reasoning is a skill published in the GitHub repository parcadei/Continuous-Claude-v3 (3,937 stars, last pushed 7mo ago), licensed MIT. It adds 14 tokens to every session and 717 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.
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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.
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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.
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".