v1.13.12: skills audit + token-tracking fix + codecontext + cap50 + UI cleanups

Multi-topic batch. The big-ticket item is the skills audit; the rest are
smaller patches that compounded during the audit work.

## Skills audit (rules→recipes split)

Vendored all 26 skills from /home/samkintop/opt/skills/ into data/skills/
(the boocode-repo-local skill library — see docker-compose change below).
Audited via 5 parallel Claude Code agent-teams running the
mgechev/skills-best-practices 4-step protocol (Discovery → Logic → Edge
Case → self-Architecture-Refinement) per skill, ~2 min wall-clock vs the
~3.7-hour serial estimate.

Result: 14 skills surviving (renamed to gerund form, frontmatter matched),
11 deleted (duplicates, BooCode-irrelevant patterns, Claude-already-does-
natively), 1 migrated to BOOCHAT.md/BOOCODER.md as an always-true rule
(verification-before-completion). Each surviving skill had its description
refined to fix specific trigger gaps surfaced by the protocol — 4
real-bug findings landed (dead refs, stale tags, broken sub-file
references in the original vendored content).

Audit decisions documented in openspec/changes/v1.13.12-skills-audit/
audit-notes.md. Convention codified in BOOCHAT.md/BOOCODER.md "rules vs
recipes" sections — future workflow rules go to those files (100%
present), recipes stay in data/skills/ (~6% invoke rate in multi-turn
per the Codeminer42 measurement).

## Token tracking + stale-stream banner fix (same root cause)

ws-frames.ts IsoTimestamp was z.string().min(1) but postgres returns
timestamp columns as JS Date objects. Every message_complete /
session_updated / chat_updated frame was failing the v1.13.11 Zod gate
and being silently dropped. Symptoms: token tracking blank in the UI
(no usage frames landed); the 60s no-token-activity timer tripped the
stale-stream banner because the frontend's local message state never
saw status='streaming' flip to 'complete'.

Fix: z.preprocess(v => v instanceof Date ? v.toISOString() : v,
z.string().min(1)) applied to the IsoTimestamp primitive. Centralized,
no publisher changes, works identically server + web (the parity test
still passes).

## Codecontext .codecontextignore auto-install

services/codecontext_client.ts now copies the
codecontext/.codecontextignore.template into any project's root on the
first call to that project if no .codecontextignore exists. One file
written per project, idempotent (in-memory Set guard + access-check),
silent fallback on read-only project. Stops the upstream empty-source-
file parser crash on foreign projects' node_modules — previously
required manually copying the template per project.

## Tool-call budget cap 30 → 50

services/inference/budget.ts: BUDGET_READ_ONLY and BUDGET_NO_AGENT
bumped to 50 (from 30). BUDGET_NON_READ_ONLY stays at 10 (no write
tools landed yet). Real recon sessions were hitting 30 with ~3 turns
wasted on codecontext parse failures; legitimate need was ~27, and
Architect-class system overviews want deeper recon. Headroom of 20
absorbs failure-retry turns without changing the safety floor — the
doom-loop guard (3 identical calls → abort) catches the actual
failure mode this cap was guarding against.

v1.14 (Phase C outer agent loop) will supersede this via per-agent
agent.steps. Throwaway-ish patch but unblocks deeper recon today.

## UI cleanups

- ChatPane queued-message dropdown removed. Each queued message now
  has three buttons: edit (pop back into ChatInput via sendToChat
  event), force-send (was the dropdown's only useful action), and
  cancel. Default behavior (send when streaming completes) needs no
  UI — it's the implicit do-nothing path.
- ChatThroughput removed from desktop tab strip (ChatTabBar.tsx).
  Mobile tab switcher still shows it.

## Plumbing

- .gitignore: data/* + !data/AGENTS.md + !data/skills/ negation
  patterns so the vendored skill library + agent registry become
  git-tracked while session DB state stays out.
- docker-compose.yml: removed /opt/skills:/data/skills override
  mount. Skills now live in the boocode repo at data/skills/,
  auditable per-batch. The host-level /opt/skills/ is preserved
  untouched for any other tools that read from it.
- .codecontextignore at repo root: auto-installed when codecontext
  was first called against /opt/boocode itself; matches the template.
- CLAUDE.md: updated to document the v1.13.11 publishFrame wrapper +
  message_parts table + tool_cost_stats view + DB-integration test
  pattern + host-side smoke endpoint quirk. (Pre-existing in working
  tree before this batch; shipped here for completeness.)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

data/skills/mattpocock/diagnosing-bugs/SKILL.md

@@ -0,0 +1,117 @@
+---
+name: diagnosing-bugs
+description: Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing/not working/something wrong, or describes a performance regression.
+---
+
+# Diagnose
+
+A discipline for hard bugs. Skip phases only when explicitly justified.
+
+When exploring the codebase, use the project's domain glossary to get a clear mental model of the relevant modules, and check ADRs in the area you're touching.
+
+## Phase 1 — Build a feedback loop
+
+**This is the skill.** Everything else is mechanical. If you have a fast, deterministic, agent-runnable pass/fail signal for the bug, you will find the cause — bisection, hypothesis-testing, and instrumentation all just consume that signal. If you don't have one, no amount of staring at code will save you.
+
+Spend disproportionate effort here. **Be aggressive. Be creative. Refuse to give up.**
+
+### Ways to construct one — try them in roughly this order
+
+1. **Failing test** at whatever seam reaches the bug — unit, integration, e2e.
+2. **Curl / HTTP script** against a running dev server.
+3. **CLI invocation** with a fixture input, diffing stdout against a known-good snapshot.
+4. **Headless browser script** (Playwright / Puppeteer) — drives the UI, asserts on DOM/console/network.
+5. **Replay a captured trace.** Save a real network request / payload / event log to disk; replay it through the code path in isolation.
+6. **Throwaway harness.** Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.
+7. **Property / fuzz loop.** If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
+8. **Bisection harness.** If the bug appeared between two known states (commit, dataset, version), automate "boot at state X, check, repeat" so you can `git bisect run` it.
+9. **Differential loop.** Run the same input through old-version vs new-version (or two configs) and diff outputs.
+10. **HITL bash script.** Last resort. If a human must click, drive _them_ with `scripts/hitl-loop.template.sh` so the loop is still structured. Captured output feeds back to you.
+
+Build the right feedback loop, and the bug is 90% fixed.
+
+### Iterate on the loop itself
+
+Treat the loop as a product. Once you have _a_ loop, ask:
+
+- Can I make it faster? (Cache setup, skip unrelated init, narrow the test scope.)
+- Can I make the signal sharper? (Assert on the specific symptom, not "didn't crash".)
+- Can I make it more deterministic? (Pin time, seed RNG, isolate filesystem, freeze network.)
+
+A 30-second flaky loop is barely better than no loop. A 2-second deterministic loop is a debugging superpower.
+
+### Non-deterministic bugs
+
+The goal is not a clean repro but a **higher reproduction rate**. Loop the trigger 100×, parallelise, add stress, narrow timing windows, inject sleeps. A 50%-flake bug is debuggable; 1% is not — keep raising the rate until it's debuggable.
+
+### When you genuinely cannot build a loop
+
+Stop and say so explicitly. List what you tried. Ask the user for: (a) access to whatever environment reproduces it, (b) a captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or (c) permission to add temporary production instrumentation. Do **not** proceed to hypothesise without a loop.
+
+Do not proceed to Phase 2 until you have a loop you believe in.
+
+## Phase 2 — Reproduce
+
+Run the loop. Watch the bug appear.
+
+Confirm:
+
+- [ ] The loop produces the failure mode the **user** described — not a different failure that happens to be nearby. Wrong bug = wrong fix.
+- [ ] The failure is reproducible across multiple runs (or, for non-deterministic bugs, reproducible at a high enough rate to debug against).
+- [ ] You have captured the exact symptom (error message, wrong output, slow timing) so later phases can verify the fix actually addresses it.
+
+Do not proceed until you reproduce the bug.
+
+## Phase 3 — Hypothesise
+
+Generate **3–5 ranked hypotheses** before testing any of them. Single-hypothesis generation anchors on the first plausible idea.
+
+Each hypothesis must be **falsifiable**: state the prediction it makes.
+
+> Format: "If <X> is the cause, then <changing Y> will make the bug disappear / <changing Z> will make it worse."
+
+If you cannot state the prediction, the hypothesis is a vibe — discard or sharpen it.
+
+**Show the ranked list to the user before testing.** They often have domain knowledge that re-ranks instantly ("we just deployed a change to #3"), or know hypotheses they've already ruled out. Cheap checkpoint, big time saver. Don't block on it — proceed with your ranking if the user is AFK.
+
+## Phase 4 — Instrument
+
+Each probe must map to a specific prediction from Phase 3. **Change one variable at a time.**
+
+Tool preference:
+
+1. **Debugger / REPL inspection** if the env supports it. One breakpoint beats ten logs.
+2. **Targeted logs** at the boundaries that distinguish hypotheses.
+3. Never "log everything and grep".
+
+**Tag every debug log** with a unique prefix, e.g. `[DEBUG-a4f2]`. Cleanup at the end becomes a single grep. Untagged logs survive; tagged logs die.
+
+**Perf branch.** For performance regressions, logs are usually wrong. Instead: establish a baseline measurement (timing harness, `performance.now()`, profiler, query plan), then bisect. Measure first, fix second.
+
+## Phase 5 — Fix + regression test
+
+Write the regression test **before the fix** — but only if there is a **correct seam** for it.
+
+A correct seam is one where the test exercises the **real bug pattern** as it occurs at the call site. If the only available seam is too shallow (single-caller test when the bug needs multiple callers, unit test that can't replicate the chain that triggered the bug), a regression test there gives false confidence.
+
+**If no correct seam exists, that itself is the finding.** Note it. The codebase architecture is preventing the bug from being locked down. Flag this for the next phase.
+
+If a correct seam exists:
+
+1. Turn the minimised repro into a failing test at that seam.
+2. Watch it fail.
+3. Apply the fix.
+4. Watch it pass.
+5. Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.
+
+## Phase 6 — Cleanup + post-mortem
+
+Required before declaring done:
+
+- [ ] Original repro no longer reproduces (re-run the Phase 1 loop)
+- [ ] Regression test passes (or absence of seam is documented)
+- [ ] All `[DEBUG-...]` instrumentation removed (`grep` the prefix)
+- [ ] Throwaway prototypes deleted (or moved to a clearly-marked debug location)
+- [ ] The hypothesis that turned out correct is stated in the commit / PR message — so the next debugger learns
+
+**Then ask: what would have prevented this bug?** If the answer involves architectural change (no good test seam, tangled callers, hidden coupling) hand off to the `/improve-codebase-architecture` skill with the specifics. Make the recommendation **after** the fix is in, not before — you have more information now than when you started.

data/skills/mattpocock/diagnosing-bugs/eval.yaml

@@ -0,0 +1,14 @@
+skill: diagnosing-bugs
+tasks:
+  - prompt: "Diagnose this: the API returns 500 every Friday afternoon but works fine other days"
+    grader:
+      - the response invokes the diagnosing-bugs skill
+      - the response asks for or constructs a feedback loop (failing test, repro script, captured trace)
+      - the response does not jump to a fix before establishing the loop
+  - prompt: "My tests have started failing intermittently after the v1.4 deploy"
+    grader:
+      - the response invokes the diagnosing-bugs skill
+      - the response addresses determinism (seeded RNG, time pinning, isolated env)
+  - prompt: "Write a haiku about autumn"
+    grader:
+      - the response does NOT invoke the diagnosing-bugs skill

data/skills/mattpocock/diagnosing-bugs/scripts/hitl-loop.template.sh

@@ -0,0 +1,41 @@
+#!/usr/bin/env bash
+# Human-in-the-loop reproduction loop.
+# Copy this file, edit the steps below, and run it.
+# The agent runs the script; the user follows prompts in their terminal.
+#
+# Usage:
+#   bash hitl-loop.template.sh
+#
+# Two helpers:
+#   step "<instruction>"          → show instruction, wait for Enter
+#   capture VAR "<question>"      → show question, read response into VAR
+#
+# At the end, captured values are printed as KEY=VALUE for the agent to parse.
+
+set -euo pipefail
+
+step() {
+  printf '\n>>> %s\n' "$1"
+  read -r -p "    [Enter when done] " _
+}
+
+capture() {
+  local var="$1" question="$2" answer
+  printf '\n>>> %s\n' "$question"
+  read -r -p "    > " answer
+  printf -v "$var" '%s' "$answer"
+}
+
+# --- edit below ---------------------------------------------------------
+
+step "Open the app at http://localhost:3000 and sign in."
+
+capture ERRORED "Click the 'Export' button. Did it throw an error? (y/n)"
+
+capture ERROR_MSG "Paste the error message (or 'none'):"
+
+# --- edit above ---------------------------------------------------------
+
+printf '\n--- Captured ---\n'
+printf 'ERRORED=%s\n' "$ERRORED"
+printf 'ERROR_MSG=%s\n' "$ERROR_MSG"