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/synaptiai/agent-capability-standard/recallnpx skills add synaptiai/agent-capability-standard --skill recallgit clone --depth 1 https://github.com/synaptiai/agent-capability-standardWrote 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/synaptiai/agent-capability-standard/recall)<a href="https://agentmods.dev/skills/synaptiai/agent-capability-standard/recall"><img src="https://agentmods.dev/badge/skills/synaptiai/agent-capability-standard/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 5.1 | $0.00038 | $0.02569 |
| Opus 5 | $0.00019 | $0.01285 |
| Sonnet 5 | $0.00008 | $0.00514 |
| Haiku 4.5 | $0.00004 | $0.00257 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intent
Execute recall to retrieve relevant prior decisions, rationale, and patterns from memory to maintain consistency and learn from past interactions.
Success criteria:
- Relevant memories are retrieved based on query
- Prior decisions and their rationale are surfaced
- Consistency with past reasoning is maintained
- Retrieved information is grounded with timestamps and context
Compatible schemas:
schemas/output_schema.yaml
Inputs
| Parameter | Required | Type | Description |
|---|---|---|---|
query |
Yes | string | What to recall: topic, decision type, or pattern name |
memory_scope |
No | enum | Where to search: session (current), project (CLAUDE.md), global (all). Default: project |
time_range |
No | object | Filter by time: { after: "2024-01-01", before: "2024-01-31" } |
include_rationale |
No | boolean | Whether to include decision rationale. Default: true |
similarity_threshold |
No | number | Minimum relevance score (0.0-1.0). Default: 0.5 |
Procedure
-
Parse query intent: Understand what is being recalled
- Identify if query is about a decision, pattern, fact, or context
- Extract key terms for memory search
- Determine if exact match or semantic similarity needed
-
Scope memory search: Identify which memory stores to query
session: Current conversation contextproject: CLAUDE.md, knowledge files, local docsglobal: All accessible memory stores
-
Search memory stores: Query relevant sources
- CLAUDE.md for project-level decisions and patterns
- Session context for recent interactions
- Knowledge files for domain-specific learnings
- Use Grep for exact term matches, Read for context
-
Rank by relevance: Score and filter results
- Relevance to query terms
- Recency (more recent = higher weight unless historical needed)
- Authority (explicit decisions > implicit patterns)
- Filter below similarity_threshold
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 316 lines · 38 tokens per session scan A b5fb6da477f8
recall is a skill published in the GitHub repository synaptiai/agent-capability-standard (4 stars, last pushed 6d ago), licensed Apache-2.0. It adds 38 tokens to every session and 2,569 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
memory-recall
Search and recall relevant memories from past sessions via memsearch. Use when the user's question could benefit from historical context, past decisions, debugging notes, previous conversations, or project knowledge -- especially questions like 'what did I decide about X', 'why did we do Y', or 'have I seen this…
alive:session-history
Revive sessions (quick or heavy), browse, and search — 'what happened recently?', 'find the session where we discussed X', 'revive yesterday's session'. For single-session recall and multi-session browsing. If the human needs to merge multiple sessions into one working context or detect conflicts between parallel…
recall
Must be used at the start of any non-trivial task involving code changes, debugging, repo exploration, file inspection, or environment/tooling investigation to surface stored guidance before analysis or tool use.
cross-session-handoff
Read, write, snapshot, and lock .arcgentic/state.yaml across planner, dev, audit, and optional test sessions.
cco-templates
Manage context templates for common task types.
knowledge-priming-refiner
Facilitate a structured conversation to create a project-specific knowledge base document. Produces a knowledge-base.md that primes AI with the project's tech stack, architecture, trusted sources, and project structure. Use when the user says 'set up knowledge base', 'prime the project', 'onboard AI', 'create…