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 Utundry/sloplesscode --skill recallgit clone --depth 1 https://github.com/Utundry/sloplesscodeWrote 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/utundry/sloplesscode/recall)<a href="https://agentmods.dev/skills/utundry/sloplesscode/recall"><img src="https://agentmods.dev/badge/skills/utundry/sloplesscode/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/utundry/sloplesscode/recall"><img src="https://agentmods.dev/badge/skills/utundry/sloplesscode/recall.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00052 | $0.00380 |
| Opus 5.5 | $0.00021 | $0.00152 |
| Sonnet 5.5 | $0.00010 | $0.00076 |
| Haiku 4.5 | $0.00005 | $0.00038 |
Grade A, and why
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 1mo 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
Recall — Search Super Memory
Search the semantic memory store for relevant saved information.
Arguments
$ARGUMENTS
Instructions
-
Parse the arguments:
- query: Search text (required)
- agent: Filter by agent ID (flag
--agent <id>, default: no filter) - type: Filter by memory type (flag
--type <type>, default: no filter) - n: Max results to return (flag
--n <n>, default: 5)
-
Call
mcp__super-memory__memory_searchwith:query: the search textagent_id: if providedmemory_type: if providedlimit: n value
-
Present results ranked by relevance score. For each result show:
- Score and similarity
- Memory type and category
- Full content
- Age (from timestamp)
-
If no results found, say so. If server is unreachable, suggest starting the services.
Examples
/recall user preferences
/recall --agent project1 database configuration
/recall --type task --n 10 pending work
/recall what does the user prefer for code style
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.
- 1mo ago First seen · 46 lines · 52 tokens per session scan A fa194190b6c7
recall is a skill published in the GitHub repository Utundry/sloplesscode (1 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 52 tokens to every session and 380 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
dreamd
Works with: Claude Code, Cursor, and Cline have adapter docs in this repo. Any MCP client can call the two tools below.
knowledge-base
Skill "knowledge-base" from Pinvou/pinvou-agent, covering ima knowledge base, operations, routing rules, examples and file upload guard.
notes
Skill "notes" from Pinvou/pinvou-agent, covering ima notes, operations, write rules, examples and response handling.
plur-create-engrams
Create or improve PLUR engrams from conversations, documents, decisions, observations, and explicit preferences. Use for memory extraction, engram authoring, or reviewing proposed memories, including global, scoped, pinned, retrieved, and provisional knowledge. Ordinary use of existing memories does not require this…
plur-memory
Persistent learning for AI agents. Open engram format. Your agent learns from corrections, remembers across sessions, and transfers knowledge across domains.
plur-session-end
Extract durable learnings at the end of a session. Saves corrections, preferences, and codebase patterns as engrams — nothing ephemeral, nothing sensitive.