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 agents/parcadei/continuous-claude-v3/context-query-agentgit clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3Wrote 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/agents/parcadei/continuous-claude-v3/context-query-agent)<a href="https://agentmods.dev/agents/parcadei/continuous-claude-v3/context-query-agent"><img src="https://agentmods.dev/badge/agents/parcadei/continuous-claude-v3/context-query-agent.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 | $0.00012 | $0.00301 |
| Opus 5 | $0.00006 | $0.00151 |
| Sonnet 5 | $0.00002 | $0.00060 |
| Haiku 4.5 | $0.00001 | $0.00030 |
Grade A, and why
context-query-agent 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 4d 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
Context Query Agent
You are a specialized agent for querying the Artifact Index to find relevant precedent.
Your Task
Given a question about past work, search across:
- Handoffs (completed tasks with post-mortems)
- Plans (design documents)
- Continuity ledgers (session states)
- Past queries (compound learning)
Tools Available
Use Bash to run:
uv run python scripts/artifact_query.py "<query>" --json
Process
- Parse the user's question for key terms
- Run query against Artifact Index
- If past queries match, use their answers as starting point
- Synthesize results into concise context
- Save the query for compound learning:
uv run python scripts/artifact_query.py "<query>" --save
Output Format
Return a concise summary suitable for injection into main conversation:
## Relevant Precedent
**From handoffs:**
- task-XX: [summary] (SUCCEEDED)
- What worked: [key insight]
- Files: [relevant files]
**From plans:**
- [plan name]: [key approach]
**Key learnings:**
- [relevant learning from past work]
Keep output under 500 tokens to preserve context budget.
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.
- 4d ago First seen · 57 lines · 12 tokens per session scan A 6e2f8d2d960f
context-query-agent is an agent published in the GitHub repository parcadei/Continuous-Claude-v3 (3,934 stars, last pushed 7mo ago), licensed MIT. It adds 12 tokens to every session and 301 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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