vectorize-io/hindsight is an agent memory system that stores experiences and helps AI agents learn over time instead of only recalling conversation history. It is used by developers adding long-term memory to agents, including coding agents. Catalogue add-ons connect agents to Hindsight and its memory workflows.
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/vectorize-io/hindsight/hindsight-recallnpx skills add vectorize-io/hindsight --skill hindsight-recallgit clone --depth 1 https://github.com/vectorize-io/hindsightWrote 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/vectorize-io/hindsight/hindsight-recall)<a href="https://agentmods.dev/skills/vectorize-io/hindsight/hindsight-recall"><img src="https://agentmods.dev/badge/skills/vectorize-io/hindsight/hindsight-recall.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 | $0.00021 | $0.00247 |
| Opus 5 | $0.00010 | $0.00123 |
| Sonnet 5 | $0.00004 | $0.00049 |
| Haiku 4.5 | $0.00002 | $0.00025 |
Grade A, and why
hindsight-recall 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 5d 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
Hindsight Recall
Trigger
Use when the user explicitly asks about past decisions, project context, preferences, or anything that may have been discussed in prior sessions.
Workflow
- Identify the key topic or question from the user's request.
- Check if
<hindsight_memories>in the current context already contains relevant results from the automatic session-start recall. - If session memory already covered it, use those memories directly.
- If the user needs more specific or deeper recall, use the Hindsight MCP
recalltool to search for additional memories. - If you want to reason over accumulated memories for architectural decisions, use the
reflecttool.
Guardrails
- Only use MCP tools for deeper recall when the session-start memory was insufficient.
- When memories conflict with current context, prefer current context and note the discrepancy.
- Do not expose raw memory metadata to the user unless asked.
Output
- Relevant memories integrated into the response
- If MCP tools are not available, advise the user to check their
.cursor/mcp.jsonconfiguration
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.
- 5d ago First seen · 30 lines · 21 tokens per session scan A 90a732e1000c
hindsight-recall is a skill published in the GitHub repository vectorize-io/hindsight (22,530 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 247 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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