recall

A skill for searching stored project memories, past sessions, and recorded learnings. It finds related information from previous work based on a topic or question.

In plain words
What is it for?
Use it to find previous decisions, implementation notes, session details, or discussions about a particular feature or file.
Why use it?
It saves time when you need earlier context but do not remember which session contained it. The results are tied to recorded observations instead of guesses.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/rohitg00/agentmemory/recall
Any agent
npx skills add rohitg00/agentmemory --skill recall
Clone the repo
git clone --depth 1 https://github.com/rohitg00/agentmemory

Made for: Claude Code, Codex.

Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 548 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00061 $0.00548
Opus 5 $0.00030 $0.00274
Sonnet 5 $0.00012 $0.00110
Haiku 4.5 $0.00006 $0.00055

Measured 3d ago against content hash 430a2527d09c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 3d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

  • recall — 88% identical, 3 lines differ
plugin/skills/recall/SKILL.md · 64 lines

What it actually says

The user wants to recall past context about: $ARGUMENTS

Quick start

memory_smart_search { "query": "jwt refresh token rotation", "limit": 10 }

Expected output:

2 results across 2 sessions.
[importance 8] decision · "Rotate refresh tokens on every use" (session 7f3a9c21)
[importance 5] code · "limit.ts counts per-IP" (session b21d004e)

Why

Only surface what the tool returned. Never fabricate an observation, a session id, or an importance score. If nothing comes back, say so.

Workflow

  1. Call memory_smart_search with the user's text as query and limit: 10. Pass project when the user scopes to a specific repo.
  2. Group results by session. Records carry a provenance channel (user, agent, tool, import, shared); when results conflict, prefer user over agent inference, and flag shared records as another teammate's write.
  3. For each observation show its type, title, and narrative.
  4. Lead with the high-signal observations (importance >= 7).
  5. If zero results, suggest 2-3 alternative search terms and stop. Do not guess.

Anti-patterns

WRONG: results are empty, so you write "We probably discussed token expiry last week" from assumption.

RIGHT: "No memories matched that query. Try refresh token, session expiry, or auth rotation."

Checklist

  • Every observation shown came from the tool response.
  • Results grouped by session, high-importance first.
  • Empty results trigger alternative-term suggestions, not invention.
  • No session id or score was paraphrased or rounded.

See also

  • remember: the write side; recall retrieves what it stores.
  • recap, handoff, session-history: session-scoped views of the same data.
  • memory-discipline: when to run this search unprompted.

Troubleshooting

See ../_shared/TROUBLESHOOTING.md if memory_smart_search is not available.

Files

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.

Changes

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.

  1. 3d ago First seen · 64 lines · 61 tokens per session scan A 430a2527d09c

Subscribe to this mod's changes

recall is a skill published in the GitHub repository rohitg00/agentmemory (27,906 stars, last pushed 2d ago), licensed Apache-2.0. It adds 61 tokens to every session and 548 once invoked, about $0.0003 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.

Related

Other skills, from other repositories

impeccable

Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states.…

Fast-Editor/Lynkr · 189 tokens

circuit

Operate Circuit, the flow engine that runs coding work as structured, evidence-backed flows (Fix, Build, Explore, Review, Prototype). Use this skill at the START of any substantive coding task in a project where Circuit is installed: fixing a bug, building a feature, refactoring, reviewing a diff or PR, investigating…

petekp/claude-code-setup · 137 tokens

latent-potential

First-principles, team-of-experts assessment of a software project that surfaces latent potential; underexploited assets, a sharper north star, missing high-leverage capabilities, better framing and messaging. Produces a prioritized, evidence-grounded report with cheap probes, a reframe candidate, a stop-doing list…

petekp/claude-code-setup · 191 tokens

claude-code-audit

Forensic audit of the user's recent Claude Code sessions to surface step-change workflow improvements — not marginal ones. Use when the user asks to "audit my Claude Code sessions", "analyze how I use Claude Code", "find patterns in my usage", "improve my Claude Code workflow", "review my sessions", "find leverage in…

petekp/claude-code-setup · 174 tokens

deep-research

Conduct exhaustive, citation-rich research on any topic using all available tools: web search, browser automation, documentation APIs, and codebase exploration. Use when asked to "research X", "find out about Y", "investigate Z", "deep dive into...", "what's the current state of...", "compare options for..."…

petekp/claude-code-setup · 119 tokens

literate-guide

Create a narrative guide to a codebase or feature in the style of Knuth's Literate Programming — code and prose interwoven as a single essay, ordered for human understanding rather than compiler needs. Use when the user asks to 'explain this codebase as a story', 'write a literate guide', 'create a narrative…

petekp/claude-code-setup · 145 tokens