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/technicalpickles/pickled-claude-pluginsWrote 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/technicalpickles/pickled-claude-plugins/connect)<a href="https://agentmods.dev/commands/technicalpickles/pickled-claude-plugins/connect"><img src="https://agentmods.dev/badge/commands/technicalpickles/pickled-claude-plugins/connect/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/technicalpickles/pickled-claude-plugins/connect"><img src="https://agentmods.dev/badge/commands/technicalpickles/pickled-claude-plugins/connect.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 | $0.00012 | $0.01454 |
| Opus 5.5 | $0.00005 | $0.00582 |
| Sonnet 5.5 | $0.00002 | $0.00291 |
| Haiku 4.5 | $0.00001 | $0.00145 |
Grade A, and why
connect 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.
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Connect Notes
Connect a note to related notes in your vault, or discover connection opportunities across the vault.
Step 1: Load Configuration
Read ~/.claude/second-brain.md for vault name and path.
If missing:
Second brain not configured. Run /second-brain:setup first.
Use the symlink path ~/.claude/vaults/{name} to access the vault (e.g., ~/.claude/vaults/primary).
Load skill references:
second-brain:obsidianfor tool mechanicsreferences/connecting.mdfor connection discovery algorithm
Step 2: Check Prerequisites
Follow references/connecting.md prerequisites:
which qmd
If qmd is not available:
qmd is not installed. Connection discovery requires qmd for semantic search.
Install via: bun add -g qmd
Then configure: qmd collection add ~/path/to/vault --name second-brain
Verify collection exists:
qmd collection list
Load collection name from ~/.claude/second-brain.md (qmd_collection setting). Default: second-brain.
Step 3: Determine Mode
If argument provided (note path): Go to Step 4 (Connect Specific Note) If no argument: Go to Step 7 (Connection Gardening)
Connect Specific Note
Step 4: Load Target Note
Resolve the path. Accept:
- Vault-relative path:
Areas/gusto/202501121430 kafka-consumer-groups.md - Absolute path:
~/Vaults/pickled-knowledge/.../file.md - Just the filename:
202501121430 kafka-consumer-groups.md(search for it)
If just a filename, find it:
find "{vault}" -name "{filename}" -type f 2>/dev/null
Read the note content.
If note not found:
Note not found: {path}
Check the path and try again.
Step 5: Run Connection Discovery
Follow the full references/connecting.md algorithm:
- Extract query terms from the note's title and body
- Run
qmd query "{terms}" -c {collection} -n 15 --json - Filter noise (self, already-linked, low-relevance, daily notes)
- Enrich candidates (check for See Also, Related, TK sections)
- Rank by connection value
- Present candidates for multi-select
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 · 209 lines · 12 tokens per session scan A 43901ced9a74
connect is a command published in the GitHub repository technicalpickles/pickled-claude-plugins (11 stars, last pushed yesterday), licensed MIT. It adds 12 tokens to every session and 1,454 once invoked, about $0.0000 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 2026-08-31.
Other commands, from other repositories
context
A project-context command that manages a .context directory containing decision notes, session logs, and history. It helps keep a record of why changes were made across Git branches and commits.
setup-context
Set up your Skill Memory — a pm-context.md every skill reads so outputs come back tailored to you.
brain
Set up or use your Professional Brain — init, ingest, recall, or review.
memory-compact
Run a dry run first.
checkpoint
Full memory flush — refresh state.md, append to activity log, route any new decisions/insights, then commit and push.
dossier
Force load of Dossier vault profile (and inbox check) before processing the rest of the user's request.