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.
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/commands/adriannoes/awesome-agentic-ai/pickup)<a href="https://agentmods.dev/commands/adriannoes/awesome-agentic-ai/pickup"><img src="https://agentmods.dev/badge/commands/adriannoes/awesome-agentic-ai/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/commands/adriannoes/awesome-agentic-ai/pickup"><img src="https://agentmods.dev/badge/commands/adriannoes/awesome-agentic-ai/pickup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00028 | $0.00391 |
| Opus 5 | $0.00014 | $0.00196 |
| Sonnet 5 | $0.00006 | $0.00078 |
| Haiku 4.5 | $0.00003 | $0.00039 |
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 5d 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.
This is a copy
100% identical to pickup — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
/pickup
Pick up where you left off on a target.
Renamed from
/resume—/resumeis a reserved Claude Code command. Use/pickupto continue a previous hunt.
What This Does
- Reads the target profile from
hunt-memory/targets/<target>.json - Shows hunt history (sessions, findings, payouts)
- Lists untested endpoints from last recon
- Suggests techniques based on tech stack + pattern DB
- Asks: continue hunting or re-run recon?
Usage
/pickup target.com
Output
PICKUP: target.com
═══════════════════════════════════════
Hunt History:
Sessions: 3
Last hunt: 2026-03-24
Total time: 2h 00m
Findings: 1 confirmed (IDOR, $1500 paid)
Untested Surface:
3 endpoints from last recon:
1. /api/v2/users/{id}/export
2. /api/v2/users/{id}/share
3. /api/v2/users/{id}/history
Memory Suggestions:
Tech stack [Next.js, GraphQL, PostgreSQL] matches 2 targets
where you found auth bypass. Try introspection → mutation pattern.
Actions:
[r] Continue hunting untested endpoints
[n] Re-run recon first (surface may have changed)
[s] Show full hunt journal for this target
If No Previous Hunt
No previous hunt data for target.com.
Run /recon target.com first, then /hunt target.com.
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.
- 5d ago First seen · 59 lines · 0 tokens per session scan A 1835c49efd85
pickup is a command published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 11d ago), licensed MIT. It adds 28 tokens to every session and 391 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to pickup, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
context-stats
Display context window usage and token statistics.
handoff
Create a handoff document for seamless session continuity.
evolve
Extract session patterns into reusable learnings.
reconcile
Reconcile learnings into .claude/rules/ proposals — on-demand version of session-end Phase 3.6.8.
lessons
Lists the feedback memories captured for the current project (and globally) — the "lessons" learned from user corrections.
memory-cleanup
Manual memory consolidation — review, consolidate, and prune memory files (Dream-equivalent).