recall

recall is a skill for Claude Code, Codex from tellahq/opensession. It costs 77 tokens per session (1,231 once invoked), scanned A, original, MIT.

A workflow for rebuilding the context of recent work from chat history and shared project records. It produces a short summary of the current state and the next steps.

In plain words
What is it for?
It is for catching up on a project, resuming interrupted work, and finding the history behind recurring bugs or production issues.
Why use it?
It helps when you have lost track of earlier decisions, fixes, incidents, or unfinished work across different conversations and tools.

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

Made for: Claude Code, Codex.

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/tellahq/opensession/recall.svg)](https://agentmods.dev/skills/tellahq/opensession/recall)
Your own site
<a href="https://agentmods.dev/skills/tellahq/opensession/recall"><img src="https://agentmods.dev/badge/skills/tellahq/opensession/recall.svg" alt="Measured on agentmods" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,231 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 $0.00077 $0.01231
Opus 5 $0.00039 $0.00616
Sonnet 5 $0.00015 $0.00246
Haiku 4.5 $0.00008 $0.00123

Measured yesterday against content hash ea5a052182ce, 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 yesterday.

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

4 near-identical copies found in the catalogue:

  • recall — 91% identical, 9 lines differ
  • recall — 89% identical, 8 lines differ
  • recall — 88% identical, 7 lines differ
  • recall — 86% identical, 6 lines differ
.agents/skills/pstack-suite/skills/recall/SKILL.md · 36 lines

How it starts

The opening of the file, as written. The whole thing — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Recall

Before you start or resume work, you rebuild the user's recent working context and hand back a tight capsule of where things stand now and what to do next. Use for "recall my work on X", "catch me up", "what have I been working on", or "where did I leave off".

Keep it tight and on-topic. Read only what the in-scope threads need, then stop. The heavy reading fans out to parallel child sessions. The main thread keeps only their findings and the final brief.

Your context lives in two records. Your own chat history holds what you did and decided. The shared record holds everything that happened around the same code under other names: the symptoms users keep reporting, the fixes that shipped and got reverted, the errors still firing in prod. That second record is what the why skill searches, across source control, the issue tracker, chat and issue channels, long-form docs, and error tracking. A feature with a long bug tail keeps most of its story there, so don't reconstruct it from your transcripts alone.

The active conversation is already in context. To inspect earlier work, discover the policy-gated Open Session session and history tools. Identify sessions by exact id and explicit creator, then read only relevant transcript windows. Never guess paths or scan session files, databases, or another project's history.

  1. Classify, then route. One specific prior chat to resume is the session-pickup playbook, not this. Turning habits into a durable skill is automate-me. A human-readable summary of your work is a different task. Recall loads working context across recent chats before you act. If the user already gave you a full state capsule (paths, branch, the change), use it and skip the mining.
  2. Lock the scope before searching. Pin the window ("recent" is a real range, default the last 7 days), the topic if named, and the workspace (default the active one; never read another project's transcripts without being asked). State the scope back. Never quietly turn "all" into "recent N".
  3. Fan out across the gated history results. Spawn parallel ask-mode /pstack children, each taking an explicit set of session ids or transcript windows. Never hand a child a filesystem transcript path. Search the topic first and read only matching windows. Skip the current chat plus obvious noise. Each child returns the same schema, one block per session: topic, the user's goal, decisions, open threads, struggles and corrections, and artifacts (PRs, tickets, branches), each citing the session id. For one or two chats, skip the fan-out and search directly. The raw transcripts stay in the child sessions. The main thread gets only their findings.
  4. Sweep the shared record whenever the topic names a feature, file, subsystem, area, or bug. This is the default, not a judgment call, and "my work on X" does not exempt it. A named target carries history you never see in your own transcripts, and that history is the point of the sweep. Hand it to the why skill's source investigators, but steer their question from "why was this built this way" to "what's the current state, what's been tried and didn't hold, and what are users still reporting". Reuse its per-source playbooks so you don't reinvent each query vocabulary, run the investigators in parallel with the chat-history mining, and inherit its posture: one investigator per source, null results are findings, skip an unavailable MCP and say so. Fold what comes back into the brief. Skip this step only for pure activity recall with no named target ("what did I do this week"), where your own history and live state are the entire answer.
  5. Verify against live state. A transcript or a stale ticket is history, not current truth, so take the PRs, branches, and tickets that the mining and the sweep surfaced and check them with git and gh. When the answer hinges on what an agent actually did (the tools it ran, files it read, errors it hit), read the full transcript, not just a trimmed local copy.
  6. Write the brief to the contract below. Group by thread. Stay on the named topic.

Read the full file on GitHub · 36 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. yesterday First seen · 36 lines · 77 tokens per session scan A ea5a052182ce

Subscribe to this mod's changes

recall is a skill published in the GitHub repository tellahq/opensession (345 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 1,231 once invoked, about $0.0004 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 2026-09-03.

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