alfa-search-recall-deepresearch

alfa-search-recall-deepresearch is a skill for Claude Code from tonydzi/second-brain-starter-kit. It costs 94 tokens per session (2,797 once invoked), scanned A, original, MIT.

A decision process for new strategic work that combines memory from a knowledge vault with outside deep research. It requires gap analysis, research, synthesis, and a written decision memo before implementation.

In plain words
What is it for?
Use it before making a significant strategic decision, such as choosing an architecture, business model, market direction, or AI feature. It is not required for quick answers, bug fixes, or tiny changes.
Why use it?
It reduces the risk of making product, architecture, market, or investment decisions from incomplete information. The process makes missing evidence and the reasoning behind a decision visible.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents; mentions Claude Code.

Part of the second-brain-skills plugin — 100 skills shipped together

Good fit Use it before making a significant strategic decision, such as choosing an architecture, business model, market direction, or AI feature. It is not required for quick answers, bug fixes, or tiny changes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tonydzi/second-brain-starter-kit/alfa-search-recall-deepresearch
Install

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.

Any agent
npx skills add tonydzi/second-brain-starter-kit --skill alfa-search-recall-deepresearch
Clone the repo
git clone --depth 1 https://github.com/tonydzi/second-brain-starter-kit

Made for: Claude Code.

Or install second-brain-skills, the plugin that ships this one along with the rest of its 100 skills.

Wrote 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.

agentmods badge for alfa-search-recall-deepresearch

README.md
[![agentmods](https://agentmods.dev/badge/skills/tonydzi/second-brain-starter-kit/alfa-search-recall-deepresearch/github.svg)](https://agentmods.dev/skills/tonydzi/second-brain-starter-kit/alfa-search-recall-deepresearch)
Your own site
<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.

agentmods 80×15 button for alfa-search-recall-deepresearch

Your own site · 80×15
<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>
Per session 94 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,797 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash ab2a89af6776, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

skills/alfa-search-recall-deepresearch/SKILL.md · 84 lines

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.

  1. Cheap-first: did SQL/grep/RAG already answer it? → done, no agents.
  2. 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? (+).
  3. 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.

Read the full file on GitHub · 84 lines

Changes

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.

  1. 2d ago Changed · -35 tokens per session ab2a89af6776
  2. 10d ago First seen · 84 lines · 129 tokens per session scan A 1d46a9bc0fc2

Subscribe to this mod's changes

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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