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 dgilford/ai-science-toolkit --skill pickupgit clone --depth 1 https://github.com/dgilford/ai-science-toolkitWrote 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/dgilford/ai-science-toolkit/pickup)<a href="https://agentmods.dev/skills/dgilford/ai-science-toolkit/pickup"><img src="https://agentmods.dev/badge/skills/dgilford/ai-science-toolkit/pickup/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dgilford/ai-science-toolkit/pickup"><img src="https://agentmods.dev/badge/skills/dgilford/ai-science-toolkit/pickup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00065 | $0.00514 |
| Opus 5.5 | $0.00026 | $0.00206 |
| Sonnet 5.5 | $0.00013 | $0.00103 |
| Haiku 4.5 | $0.00006 | $0.00051 |
Grade A, and why
pickup 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 1mo 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.
What it actually says
Resume this project from the repo-local handoff state.
Current repo state
git status --short 2>/dev/null || echo "(not a git repo)"
git log --oneline -12 2>/dev/null || echo "(no git log)"
Instructions
Check for .ai/HANDOFF.md. Two paths:
If found: Read it, along with CLAUDE.md and AGENTS.md if present. Then verify it against current repo state: confirm the branch matches, check that referenced files exist, and flag any commits more recent than the handoff that aren't reflected in it. If .ai/reviews/ exists, note the most recent review report and surface any of its unresolved findings that bear on the next action.
If not found: Tell the user explicitly that no handoff was found before doing anything else. Then reconstruct best-effort from the repo: scan recent git log, read recently modified files, and check for obvious entry points (scripts, notebooks, configs).
Report
State on the first line whether the handoff was loaded: Handoff loaded from .ai/HANDOFF.md or No handoff found — reconstructed from repo.
Then open with a 2–3 sentence conversational recap — what this project is, what was being worked on, and where things stand. Write it the way a colleague would catch someone up after they've been away, not as a bullet list.
Then follow with only the sections that have real content:
Key decisions — consequential choices already locked in.
Scientific context — datasets, baselines, reference periods, counterfactual definitions, known data quality issues.
Risks — load-bearing assumptions, things likely to go wrong.
Next action — the one concrete thing to do right now. If multiple paths are plausible, compare them in 2–3 lines each and recommend one.
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.
- 1mo ago First seen · 46 lines · 65 tokens per session scan A fae3a3957ba5
pickup is a skill published in the GitHub repository dgilford/ai-science-toolkit (74 stars, last pushed yesterday), licensed MIT. It adds 65 tokens to every session and 514 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
nav-compact
Clear conversation context while preserving knowledge via context marker. Use when user says "clear context", "start fresh", "done with this task", or when approaching token limits.
hive-mind-advanced
Queen-led collective-intelligence coordination with consensus mechanisms and persistent shared memory. Use when orchestrating a large swarm of agents under a lead coordinator, building consensus across agents, or maintaining durable cross-agent memory.
creator-productivity
Task management and workspace memory for creative work sessions. Manages a TASKS.md file for project tracking, a two-tier memory system (CREATOR.md + creator-memory/), and a daily sprint structure. Use when organizing creative projects, tracking content tasks, or starting a focused work session.
memory-prune
Manual reviewable prune of stale memory entries. Lists project entries older than 30 days with content preview; Frank decides per-entry [keep | archive | pin]. Keeps MEMORY.md under the auto-load 200-line truncation. Use when MEMORY.md exceeds 180 lines or session-end after a heavy work week.
creator-intelligence
Transform Claude Code into your personal Jarvis - an AI operating system that knows your projects, preferences, and workflows across sessions. Use this skill when setting up personal AI systems, building context-aware assistants, or creating persistent memory across projects.
fresh-start
Continue in a new chat without losing anything — writes a compact handoff to paste into a fresh conversation. Use whenever the user wants to move to a new chat or conversation, hits usage limits, says replies got slow or Claude forgets earlier details, or the conversation has grown long.