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 ScottRBK/forgetful --skill forgetful-recallgit clone --depth 1 https://github.com/ScottRBK/forgetfulWrote 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/scottrbk/forgetful/forgetful-recall)<a href="https://agentmods.dev/skills/scottrbk/forgetful/forgetful-recall"><img src="https://agentmods.dev/badge/skills/scottrbk/forgetful/forgetful-recall/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/scottrbk/forgetful/forgetful-recall"><img src="https://agentmods.dev/badge/skills/scottrbk/forgetful/forgetful-recall.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00078 | $0.00971 |
| Opus 5 | $0.00039 | $0.00485 |
| Sonnet 5 | $0.00016 | $0.00194 |
| Haiku 4.5 | $0.00008 | $0.00097 |
Grade A, and why
forgetful-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 10d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recalling knowledge from Forgetful
Retrieval quality is decided by how the query is shaped and scoped, and by treating coverage as something to judge rather than assume. Recall before proposing; history usually exists.
Invoking operations
Operations are named by registry name (query_memory, get_recent_memories, ...). Invoke
via whichever surface this agent has:
- MCP:
execute_forgetful_tool(tool_name="query_memory", arguments={...}) - CLI:
forgetful call query_memory --args '{"query": "..."}' --json
Get any operation's schema at runtime: how_to_use_forgetful_tool (MCP) or
forgetful tools info <operation> (CLI) — schemas are deliberately not repeated here.
Step 1 — Shape the query
query_context is a required parameter alongside query, not optional flavor text — the
call errors without it. Pass it deliberately: the two are embedded together, and ranking
genuinely shifts with intent ("auth" while implementing a feature ranks differently than
"auth" while debugging login). Include exact identifiers verbatim — error codes, function
names, config keys — the sparse full-text leg of the search matches them literally.
Done when: both query and query_context are written, not just a bare keyword.
Step 2 — Scope deliberately
Reads are cross-project by default, and usually should stay that way — knowledge transfers.
Narrow with project_ids when the task is project-bound; add strict_project_filter=True
to also keep linked memories inside those projects (the default False lets links cross
them). Use importance_threshold to cut noise — it excludes anything scored below the value
given, pairing naturally with forgetful-remember's rubric, where 5 is the noise floor for
bulk/automated captures. Adjust k to trade breadth for focus. These are filters layered on
top of semantic search, which stays the primary retrieval mechanism throughout.
Done when: the scope is a choice, not a default accident.
Step 3 — Judge coverage
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.
- 10d ago First seen · 94 lines · 78 tokens per session scan A f21b771e8f88
forgetful-recall is a skill published in the GitHub repository ScottRBK/forgetful (299 stars, last pushed 7d ago), licensed MIT. It adds 78 tokens to every session and 971 once invoked, about $0.0004 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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