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 tonydzi/second-brain-starter-kit --skill alfa-search-recall-deepresearchgit clone --depth 1 https://github.com/tonydzi/second-brain-starter-kitWrote 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/tonydzi/second-brain-starter-kit/alfa-search-recall-deepresearch)<a href="https://agentmods.dev/skills/tonydzi/second-brain-starter-kit/alfa-search-recall-deepresearch"><img src="https://agentmods.dev/badge/skills/tonydzi/second-brain-starter-kit/alfa-search-recall-deepresearch/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/tonydzi/second-brain-starter-kit/alfa-search-recall-deepresearch"><img src="https://agentmods.dev/badge/skills/tonydzi/second-brain-starter-kit/alfa-search-recall-deepresearch.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 | $0.00094 | $0.02797 |
| Opus 5 | $0.00047 | $0.01399 |
| Sonnet 5 | $0.00019 | $0.00559 |
| Haiku 4.5 | $0.00009 | $0.00280 |
Grade A, and why
alfa-search-recall-deepresearch 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 2d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🅰️ Alpha Protocol — Recall + Deep Research before deciding
🧒 When reporting to the operator: end with a child-simple "In plain words" recap (his standing request; reports TO the operator only).
Binding rule (must): For ANY new strategic function, product, business model, tokenomics, AI-functionality, GTM, market, investment hypothesis, or architecture decision — it is FORBIDDEN to go to implementation on recall alone. Do Recall → Gap → Deep Research → Synthesis → Decision Memo first. Recall is necessary but NOT sufficient for strategic work. Canon: vault note protocol-alpha-protocol-recall-plus-deep-research.
When this fires
- The operator writes the trigger:
R+DR(=RDR—+/space & case don't matter),alpha protocol(or/alpha). - OR you are about to start a Level-2 task (below) — invoke this protocol proactively, don't wait for the trigger.
Levels (size the response to the task)
- L0 — Quick (answer a question, fix a bug, tiny tweak): plain recall is enough. No DR.
- L1 — Recall (new module / hypothesis / feature / market): recall memory + internal docs + past research + form hypotheses. Do NOT decide immediately. Usually no external DR unless it turns strategic.
- L2 — Recall + DR (strategic — see binding rule): run the full flow below.
★ Proactive multi-agent reflex (EVERY task, not just strategic) — set 2026-06-25
The user FORGETS whether they need agents — so YOU remember and propose, reflexively, after RECALL on any task. Canon: vault reglament-proaktivno-predlagay-agentov + memory multi-agent-offer-reflex; tool-choice canon = decision-adopt-agent-teams-scoped.
- Cheap-first: did SQL/grep/RAG already answer it? → done, no agents.
- Type = Decision · Comparison · Analysis · Research-synthesis where several INDEPENDENT lenses materially improve the answer (inclusion test: will one lens's finding redirect another before both finish?)? NO (import/fix/ops/mechanical/trivial) → single agent, stay silent about agents. YES → multi-agent fits → fork:
- AUTO-RUN (announce, don't ask): read-only · no vault write · no outbound · ~2 Sonnet agents · not huge → spawn advocate↔skeptic (or champion-X↔champion-Y, or N orthogonal lenses) then synthesize. First line:
🤝 Spawning 2 Sonnet agents (read-only)…. - OFFER + ASK (
+): vault-write/outbound · ≥3 agents or long/expensive · strategic-irreversible (→ full R+DR L2 below) · money/secrets/Tier-2. One line:🤝 Agents: I recommend (…); shall I spawn them? (+).
- AUTO-RUN (announce, don't ask): read-only · no vault write · no outbound · ~2 Sonnet agents · not huge → spawn advocate↔skeptic (or champion-X↔champion-Y, or N orthogonal lenses) then synthesize. First line:
- Teammates on Sonnet; don't auto-keep an Opus lead; never ask for "consensus" — preserve dissent. Mechanism = Agent-tool subagents (cheap, ~80% of the value) or native Agent Teams when enabled.
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.
- 2d ago Changed · -35 tokens per session ab2a89af6776
- 10d ago First seen · 84 lines · 129 tokens per session scan A 1d46a9bc0fc2
alfa-search-recall-deepresearch is a skill published in the GitHub repository tonydzi/second-brain-starter-kit (6 stars, last pushed 2d ago), licensed MIT. It adds 94 tokens to every session and 2,797 once invoked, about $0.0005 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 2026-08-31.
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