load-work

load-work is a skill for Claude Code from digital-stoic-org/agent-skills. It costs 38 tokens per session (973 once invoked), scanned A, original, MIT.

A guided menu for finding and reopening saved coding sessions. It reads context files that record earlier work, including the session's status, focus, date, and goal.

In plain words
What is it for?
Use it when you want to resume work, continue from an earlier session, or see what you were working on. It supports context files saved by both Cowork and dstoic.
Why use it?
It removes the need to remember which work was left unfinished or manually search through saved context files. It also distinguishes current sessions from archived ones.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: model in frontmatter; names the AskUserQuestion tool.

Part of the cowork plugin — 5 skills shipped together

Good fit Use it when you want to resume work, continue from an earlier session, or see what you were working on. It supports context files saved by both Cowork and dstoic.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/digital-stoic-org/agent-skills/load-work
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 digital-stoic-org/agent-skills --skill load-work
Clone the repo
git clone --depth 1 https://github.com/digital-stoic-org/agent-skills

Made for: Claude Code.

Or install cowork, the plugin that ships this one along with the rest of its 5 skills.

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 load-work

README.md
[![agentmods](https://agentmods.dev/badge/skills/digital-stoic-org/agent-skills/load-work.svg)](https://agentmods.dev/skills/digital-stoic-org/agent-skills/load-work)
Your own site
<a href="https://agentmods.dev/skills/digital-stoic-org/agent-skills/load-work"><img src="https://agentmods.dev/badge/skills/digital-stoic-org/agent-skills/load-work.svg" alt="Measured on agentmods" height="20"></a>
Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 973 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 5
    Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.
    Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 $0.00038 $0.00973
Opus 5 $0.00019 $0.00487
Sonnet 5 $0.00008 $0.00195
Haiku 4.5 $0.00004 $0.00097

Measured 8d ago against content hash 60ae74f54451, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

load-work 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.

cowork/skills/load-work/SKILL.md · 123 lines

How it starts

The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Load Session

Menu-driven session resume for non-CLI interfaces (Cowork desktop, Telegram, CC CLI). Reads CONTEXT files written by both /save-work (cowork) and /save-context (dstoic) — same format.

Phase 1: Scan Available Sessions

Run both Glob calls in parallel (single tool message):

Glob: CONTEXT-*-llm.md (current dir)
Glob: done/CONTEXT-*-llm.md (archived)

For each file found, Read first 10 lines to extract header:

  • stream name (from filename: CONTEXT-{name}-llm.md{name}, CONTEXT-llm.mddefault)
  • status
  • focus
  • saved date
  • goal (if present)

⚠️ AskUserQuestion Guard

After EVERY AskUserQuestion call, check if answers are empty/blank. If empty: output "⚠️ Questions didn't display (known bug).", present options as numbered text list, WAIT for user reply.

Phase 2: Present Menu

Sort by saved date (most recent first). Separate active from archived.

📂 Available sessions:

📍 Active
  1. 🔄 {stream-1} ({relative-date}, {status})
     → {focus}
  2. 🏗️ {stream-2} ({relative-date}, {status})
     → {focus}

📦 Archived
  3. ✅ {stream-3} ({relative-date}, {status})
     → {focus}

Resume which? (number) or 0 to start fresh

If only 1 session exists → still show menu but suggest it: Resume **{stream}**? (yes / no)

If 0 sessions found → No saved sessions found. Use /switch to select a project, or start working and /save when ready.

Wait for user reply.

Phase 3: Load Selected Session

If user picks a number → Read the full CONTEXT file. If user picks 0 → respond: Starting fresh. What would you like to work on?

If session was from done/ → note: 📦 This session was archived (status: {status}). Resuming will create a new active session.

Parse CONTEXT file sections:

  • Header (stream, saved, status, focus, goal)
  • Project
  • Next Tasks
  • Session (progression, decisions, thinking, unexpected)
  • Hot Files
  • Refs

Phase 4: Present Resume Report

📍 Resumed: {stream name}
⏰ Last saved: {date, relative}
🎯 Focus: {focus}
🎯 Goal: {goal}

📋 Where you left off:
  {session progression — 2-3 key bullets}

🔑 Decisions made:
  {decisions — bullets if any}

✅ Next tasks:
  1. {task 1}
  2. {task 2}
  3. {task 3}

🔥 Key files:
  - {file}: {role}

What would you like to tackle first?

Read the full file on GitHub · 123 lines

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. 8d ago First seen · 123 lines · 38 tokens per session scan A 60ae74f54451

Subscribe to this mod's changes

load-work is a skill published in the GitHub repository digital-stoic-org/agent-skills (20 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 973 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 2026-08-30.

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