Borrowing it
Nothing to install: this file belongs to agentculture/fleet-cli. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/agentculture/fleet-cli/main/.claude/skills/recall/SKILL.mdgit clone --depth 1 https://github.com/agentculture/fleet-cliWrote 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/agentculture/fleet-cli/recall)<a href="https://agentmods.dev/skills/agentculture/fleet-cli/recall"><img src="https://agentmods.dev/badge/skills/agentculture/fleet-cli/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/agentculture/fleet-cli/recall"><img src="https://agentmods.dev/badge/skills/agentculture/fleet-cli/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 | $0.00279 | $0.02365 |
| Opus 5.5 | $0.00112 | $0.00946 |
| Sonnet 5.5 | $0.00056 | $0.00473 |
| Haiku 4.5 | $0.00028 | $0.00236 |
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 2mo 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.
This is a copy
97% identical to recall — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
recall — search the shared eidetic memory
recall drives eidetic recall: given a query, it returns the top-k stored
records ranked by relevance, each with its text, full metadata (provenance),
a numeric score, and a freshness signal. It is the read half of the memory
surface; the write half is the sibling /remember skill.
The point of a shared store is that memory is a team faculty, not a
per-agent silo: a record Claude wrote is recallable by the colleague backend
(and vice versa), because both resolve the same ~/.eidetic/memory path.
How to run
bash .claude/skills/recall/scripts/recall.sh "<query>" [flags...]
The wrapper resolves the CLI portably (installed eidetic on PATH, else
uv run eidetic from the checkout) and forwards every flag verbatim, so it is
exactly eidetic recall …. Run it from anywhere; the store is the same.
Search modes (--mode, default hybrid)
| Mode | What it matches | Needs embed server? |
|---|---|---|
exact |
case-insensitive verbatim substring (--case-sensitive to tighten) |
no — offline-safe |
approximate |
vector cosine / semantic similarity | yes (falls back offline) |
keyword |
BM25 lexical; only records sharing a query term | no — offline-safe |
hybrid |
alpha*approximate + (1-alpha)*keyword (--alpha, default 0.5) |
uses it when up |
hybrid is the default because the two signals cover each other's blind spots:
vector catches paraphrases, keyword catches exact ids/quotes. When the embed
server is unreachable, hybrid collapses to keyword-only (it never fuses
meaningless offline-fallback cosine).
Output fields
Each hit in --json output includes:
| Field | Notes |
|---|---|
id |
stable record identity |
text |
the stored chunk |
type |
record type |
metadata |
full provenance, round-tripped verbatim from ingest |
score |
relevance score from the chosen search mode (freshness-blended) |
signal |
freshness strength in [0, 1]; computed at recall time from age, recall frequency, and staleness |
created |
ISO-8601 ingest date (may be DATE_UNKNOWN for legacy records) |
last_recall |
ISO-8601 timestamp of the most recent recall hit (null if never recalled) |
recall_count |
number of times this record has been recalled (passive reinforcement counter) |
lifecycle |
active, shadowed, or archived |
links |
list of related-memory ids |
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.
- 2mo ago First seen · 182 lines · 279 tokens per session scan A ec8443a7b567
recall is a skill published in the GitHub repository agentculture/fleet-cli (2 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 279 tokens to every session and 2,365 once invoked, about $0.0011 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to recall, differing in 4 lines, and is treated as a copy.
Other skills, from other repositories
knowledge-ingest
Ingest documents, articles, conversations, or supplied text into Knowledge with retained originals, sourced claims, entity updates, source notes, and cross-links; also learn durable lessons from a set of conversations, transcripts, or past sessions (/learn), including the Learn from conversations and Learn from Memory…
mem0-oss-to-platform
Plan, then execute, a migration of a project from the mem0 open-source / self-hosted SDK (local Memory class) to the mem0 Platform / hosted SDK (MemoryClient). TRIGGER when: the developer wants to move, switch, or migrate mem0 off OSS/self-hosted to the hosted API, e.g. "migrate my mem0 setup to the platform", "switch…
forget
Remove a personal fact, preference, relationship detail, topic, or prior event from Muse's active memory and stop existing copies or automations from bringing it back. Use for explicit requests such as 'forget that', 'don't remember this about me', or 'remove that from your memory'. Do not use when 'forget it' merely…
Cortex
LifeOS memory, prior-session recall, and learning capture. USE WHEN capture this learning, document this learning, what did we learn recently, recent learnings, cortex, knowledge, knowledge base, search knowledge, what do we know about, archive, harvest, knowledge status, develop note, add to knowledge, add a people…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…