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.
npx skills add digital-stoic-org/agent-skills --skill load-workgit clone --depth 1 https://github.com/digital-stoic-org/agent-skillsWrote 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/digital-stoic-org/agent-skills/load-work)<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>- NVIDIA SkillSpector warn
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.
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 | $0.00038 | $0.00973 |
| Opus 5 | $0.00019 | $0.00487 |
| Sonnet 5 | $0.00008 | $0.00195 |
| Haiku 4.5 | $0.00004 | $0.00097 |
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.
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.md→default) - 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?
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.
- 8d ago First seen · 123 lines · 38 tokens per session scan A 60ae74f54451
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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Your team's shared, AI-ready knowledge base — people, companies, meetings, SOPs, and decisions structured so Claude can answer questions on your team's behalf. Team-scope sibling to second-brain (which is personal-scope). Seven modes — capture (drop something into the right structured dir), compile (process into wiki…
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