recall

recall is a skill for Claude Code from nicknisi/sessions. It costs 141 tokens per session (724 once invoked), scanned A, original, MIT.

A project-history lookup that finds relevant past AI coding sessions and summarizes what happened. It can focus on a question, a project, or a particular file.

In plain words
What is it for?
Use it to recall why a change was made, find where work stopped, answer whether an issue was already fixed, or investigate what happened to a file.
Why use it?
It removes the need to manually scan old sessions when you need to remember a decision, locate unfinished work, or trace a file's history.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Part of the sessions plugin — 7 skills, 1 MCP server shipped together

Good fit Use it to recall why a change was made, find where work stopped, answer whether an issue was already fixed, or investigate what happened to a file.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nicknisi/sessions/recall
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.

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

Made for: Claude Code.

Or install sessions, the plugin that ships this one along with the rest of its 7 skills, 1 MCP server.

Wrote 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.

agentmods badge for recall

README.md
[![agentmods](https://agentmods.dev/badge/skills/nicknisi/sessions/recall.svg)](https://agentmods.dev/skills/nicknisi/sessions/recall)
Your own site
<a href="https://agentmods.dev/skills/nicknisi/sessions/recall"><img src="https://agentmods.dev/badge/skills/nicknisi/sessions/recall.svg" alt="Measured on agentmods" height="20"></a>
Per session 141 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 724 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 7
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
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 5.1 $0.00141 $0.00724
Opus 5 $0.00071 $0.00362
Sonnet 5 $0.00028 $0.00145
Haiku 4.5 $0.00014 $0.00072

Measured 8d ago against content hash 16832f50cda1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, 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 8d 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.

plugin/skills/recall/SKILL.md · 52 lines

How it starts

The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Recall past work on a specific project or topic.

Steps

  1. Classify the question. Each shape has a cheapest path:

    • Needle — "what did we decide about X?" → search, then read just the matched exchange.
    • Arc — "where did we leave off on X?" → digest the relevant session(s).
    • Artifact — "what happened to this file?" → search with the files filter.
  2. Find candidate sessions. Call search_sessions:

    • Needle/arc: query = topic keywords; add project if a path was given; limit: 10.
    • Artifact: files: [path or suffix], no query — results come back newest first.

    Each result carries messageHits — the specific messages that matched (index, role, snippet). The snippets alone often answer a needle question.

  3. Digest the best 1-3 candidates. Call get_session_digest with each candidate's filePath. One bounded call (~2k tokens) returns the session's arc: every genuine user turn paired with its exchange's final assistant reply, each with a message index. For "where did we leave off", the last exchange of the newest session's digest usually is the answer.

  4. Expand only what you need. To read around a specific point, call get_session_messages with offset = a messageHits[].index (from search) or exchanges[].index (from digest) and a small limit (5-10). Never page a transcript from offset 0 when a hit or digest index is available.

  5. Summarize the history. Write a chronological summary:

    Sessions on {project/topic}

    For each relevant session:

    • {date} ({tool}) — What was worked on, key decisions made, outcome

    Overall arc: How the work evolved across sessions.

Guidelines

  • Budget discipline: digests are bounded, transcripts are not. Prefer one digest over five pages of messages; expand at most the exchanges you need.
  • If a digest returns empty exchanges (no genuine human turns), fall back to get_session_messages.
  • Order chronologically (oldest first) to show the arc of work.
  • For vague date-range asks ("what was I doing last week?"), use get_activity_digest with a date range instead of search.
  • Focus on decisions and outcomes, not implementation details.
  • If there are many sessions, group by phase or milestone rather than listing each one.

Read the full file on GitHub · 52 lines

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. 8d ago First seen · 52 lines · 141 tokens per session scan A 16832f50cda1

Subscribe to this mod's changes

recall is a skill published in the GitHub repository nicknisi/sessions (30 stars, last pushed 6d ago), licensed MIT. It adds 141 tokens to every session and 724 once invoked, about $0.0007 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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