agentleFS
tonydzitonydzi/second-brain-starter-kit/skills/intake/SKILL.md

intake

Skill6 starsChanged 2 months agoMITView on GitHub

Route a new rule, preference or policy edit into every home it belongs in (always-loaded canon, memory, the behavioral codex, a skill, a hook) and leave a trace in the always-loaded layer so parallel sessions pick it up on their own. Flow: dedup recall, classify by form, write to all homes, back up, report. Triggers: "/intake", "record this rule", "route this to its homes".

34 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use plan, triage and 6 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • BlockSkill

    plan

    Draft a TDD, PRD, or lightweight plan in the conversation without committing it.

    Also in 4 projects that use this

  • Google CloudSkill

    triage

    Gather context on a change or bug and plan the implementation.

    Also in 4 projects that use this

  • Microsoft AzureSkill

    build

    Build the TypeSpec Rust emitter from source. Run this after cloning the repo, after pulling changes, or when TypeScript source files have been modified.

    Also in 3 projects that use this

  • VercelSkill

    deploy

    Deploy applications to Vercel. Use when the user says "deploy", "deploy to Vercel", "push to production", "deploy my app", or "go live".

    Also in 3 projects that use this

  • Microsoft AzureSkill

    verify

    End-to-end app testing pipeline for browser-based web apps on cloud browsers via Azure Playwright Workspaces: verify the live app, author tests for working flows, heal failing tests, then re-run with video and produce one consolidated report. Three phases (app verification, test authoring, test healing) plus a scoped final sanity run; each phase emits a sub-report. Use when asked to "verify my app", "make sure my app works and has good test coverage", "do a full QA pass", "add tests where I'm missing coverage", "fix failing tests and add new ones", "smoke test before deploy", "QA my deployment", or any combined verify-and-test ask. Requires Azure CLI (`az login`) and a Playwright Workspace resource ID — bootstraps configs on first invocation. Browser-based web apps with Playwright only — not for unit/API tests, native mobile/desktop, load/perf, or full accessibility audits.

    Also in 4 projects that use this

  • VercelSkill

    setup

    Set up Vercel CLI and project configuration. Use when the user says "set up Vercel", "configure Vercel", "link to Vercel", or "vercel init".

    Also in 4 projects that use this

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 2 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 33 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Before writing — check whether such a rule already exists (update it, don't spawn a copy.

    line 23
  2. Very specific:

    Memory: create/update memory\<slug>.md with frontmatter (name, description, metadata.type: user/feedback/project/reference).

    line 44
  3. Distinctive:

    Always RECALL before writing (Step 0) — update what exists instead of spawning a second copy.

    line 63

“Common” is measured: kept by at least 6 in 10 of the 33 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

/intake — the rule intake (route it to its homes, don't just write it down)

🧒 When reporting to a non-technical owner: end with a child-simple "In plain words" recap (memory eli5-always). Reports TO him only — never inside vault notes.

Why (canon: memory rules-intake-channel). This chat is the dedicated channel where the owner drops ALL rules/policies/Bible entries/skills/CLAUDE.md edits/preferences, so that working chats stay clean. My job is not just to write it down, but to DELIVER the rule to all of its homes, cross-link it, and leave a trace in the always-loaded layer, so that automatic/parallel processes see it on their next start. This skill = the same manual flow I repeated ~10× in one session, folded into a single command.

Architecture (skill-design-three-layer): a thin SKILL.md orchestrator + deterministic steps (backup/commit/grep) + an external store (CLAUDE.md, memory, the vault). The routing decision is still shown to the owner in the report — the skill does not hide the logic, it executes it without asking at every step.


Step 0 — RECALL (dedup; cheap, ~0 tokens). NEVER spawn a duplicate

Before writing — check whether such a rule already exists (update it, don't spawn a copy; resolve conflicts by "newer beats older"):

  • MEMORY.md is already in context — scan its index by topic.
  • grep over memory: $USERPROFILE/.claude/projects/<project>/memory/.
  • grep over the Bible/vault: reglament-* / protocol-* under 03-Insights\Operations\ and 05-Resources\Protocols\; if needed, RAG via brain_ask.py "<topic>".
  • Found an existing one → update it (don't create a second), mark supersedes / refresh date_established. A conflict between the owner's own rules (origin: anton) is resolved only by him or by an explicit supersedes.

Step 1 — Classify the rule by its FORM → pick the homes

Route per operating-agreement → "Where durable rules go" (do NOT duplicate the rule across files — each level REFERENCES the one below):

  • How I work / a machine process (imports, dashboards, token economy, routines, my reply style) → CLAUDE.md (an always-on block) + memory (+ a line in MEMORY.md).
  • A durable fact / preference → memory (memory\*.md + a pointer line in MEMORY.md).
  • An action ON THE OWNER'S BEHALF towards the outside world (outreach, his chats, calendar, purchases, hiring, household) → the Bible (reglament-* in the vault) + if needed a lift into always-on in CLAUDE.md.
  • "Every time automatically when X" → a hook (skill update-config), not "keep it in mind".
  • Time-based "every Monday / every morning" → a scheduled task / routine (skill schedule).

Step 1★ — THE TEST "could a HUMAN do this?" → YES → the rule ALSO GOES INTO THE BIBLE

If the rule could hypothetically be executed by the owner's live assistant (not only by me/code) — it is duplicated into the Bible (the single rulebook for all actors: the owner + assistants + AI). To keep the copies from drifting: the canon of the human-executable part lives in the Bible, and the machine layer (CLAUDE.md/memory) REFERENCES it. Purely mechanical things (an import script, a reindex — no human does that) are NOT duplicated into the Bible.

Step 2 — THE KEY invariant: a trace in the always-loaded layer is MANDATORY

The only things that "surface by themselves" for parallel processes are the ones in the always-loaded layer: CLAUDE.md and the MEMORY.md index load into EVERY session; individual memory files load by relevance; the vault does NOT surface on its own (it needs grep/RAG); a skill surfaces on its trigger. → For a rule to become "background/automatic", it MUST leave a trace in CLAUDE.md or MEMORY.md (at least a pointer line to the canon), not only in the vault/skill.

Step 3 — Write into every chosen home

  • Memory: create/update memory\<slug>.md with frontmatter (name, description, metadata.type: user/feedback/project/reference). Body: for feedback/project — **Why:** + **How to apply:**. Use [[name]] links liberally. + one pointer line in MEMORY.md (- [Title](file.md) — hook, ≤200 chars).
  • CLAUDE.md: if it is always-on — add a block ## ALWAYS: <topic> (standing — set <date>) or append a bullet to an existing one; finish with "Canon: memory <slug> + Bible <reglament>". ⚠️ Entry budget: the block = ONLY trigger + gist + pointer, target ≤4 lines / ~900 characters; the mechanics (commands, formats, IDs, examples) live in the Bible/memory/skill, they have no place in CLAUDE.md. ⭐ PAY THE ENTRY FEE (Bible entry on optimising always-loaded files): BEFORE writing, run python ~/.claude/scripts/claude_md_guard.py --preflight <block.md> — exit 0 = go ahead; exit 1 = the file is in the yellow/red zone, first free up at least the block's size (compress a section with the harness claude_md_compress.py index→build→verify / move the body into the Bible), then write. We do not re-compress at the word level (declined) — structure only. The write hook on CLAUDE.md will repeat the warning, but the preflight is your step, don't wait for the hook.
  • The Bible (if Step 1★ applies): a reglament-*.md in 03-Insights\Operations\ (or the right domain) in the protocol-bible-as-prompt format (the owner verbatim, WHEN → DO, frontmatter with audience/origin/authored_by/date_established/status/confidence). Wire it into the MOC (_Operations-Bible-MOC or the domain index) — otherwise the assistant will never find it.

Step 4 — Backup + selective commit (safety)

  • Writing into the vault → FIRST vault_backup.py ([[vault-backup-rule]]).
  • ⚠️ If the shared backup is blocked (the fuse catches someone else's mass deletions from a parallel fleet run) — do NOT force it; commit ONLY your own files: cd $OBSIDIAN_VAULT && git add "<my file>" && git commit -m "...". Never --force, never a blanket glob delete.
  • CLAUDE.md/memory are auto-committed by git (claude-skills-git-backup, a 15-minute task) — no separate commit needed.

Step 4★ — THE HOMES MATRIX + the watchdog (a gate, not an option; canon: the Bible entry on writing lessons into every home)

Before the report, fill in the matrix: each of the 6 homes (Bible · CLAUDE.md · memory + MEMORY.md · skill · hook/task · MOC) gets an explicit verdict ✅ written / ⛔ not needed + reason — silently skipping a home is not allowed (pitfall 2026-07-14: the Bible was forgotten because the route was kept "in the head"). Then prove it with the counter: python ~/.claude/scripts/rule_home_guard.py <slug/keywords> — exit 1 = no trace in the always-loaded layer (Step 2 violated) → fix it before reporting.

Step 5 — Report after the fact (NOT for permission — for transparency)

Briefly to the owner: WHAT the rule is · THE HOMES MATRIX from Step 4★ (a verdict per home) · what was cross-linked · conflicts/duplicates (if an existing one was updated). Like with concepts — after the fact, without asking permission for each home.


Boundaries / don't duplicate

  • Always RECALL before writing (Step 0) — update what exists instead of spawning a second copy.
  • The canon of this procedure = memory rules-intake-channel (this skill is its executable form). The mirror is capture-rules-into-bible (which catches rules in ANY chat; intake = the dedicated channel + the always-loaded-layer mechanics).
  • Secrets (passwords/access/financial figures/"grey" techniques) — NEVER in the loaded layer (CLAUDE.md/MEMORY.md/the Bible); their home is secrets\ (memory credential-store).

<!--kit-footer-->

Like this skill? It is one of 100 in second-brain-starter-kit: the second brain we built for ourselves and run every day at Palo Alto AI Research Lab. Install the whole set with npx skills add tonydzi/second-brain-starter-kit. Everything is open source and free, so take what you need.

Flagships worth a look on their own: secondop-panel (a second opinion from a panel of external models), claude-memory-tidy (stop your agent's memory from rotting), telegram-mcp-kit (your own Telegram over MCP in about 15 minutes).

Author: Anton Dziatkovskii, Palo Alto AI Research Lab. Telegram @tonydzi - WhatsApp +1 341 222 9178 - X @Tony_Stef_

Engineers: want to test-drive this setup? Message me. I hand out free starter seeds to engineers who test and report back, and custom skill requests are welcome.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.