rapidfuzz's latest release at time of writing (3.14.4) has no
win_amd64 wheel published, which makes `uv sync` fail outright on
Windows (no sdist build toolchain assumed). Overriding to 3.13.0,
the latest version with a Windows wheel, unblocks installation.

This can be relaxed/removed once upstream publishes Windows wheels
for a newer version again.

interview_agents() (used by the report generator to interview the
top N most-active agents post-simulation) sends a batch IPC command
with a hardcoded 180s timeout covering up to N agents x 2 platforms
of individual LLM interview calls.

With CLI-based LLM providers this was consistently timing out with
0/N successful interviews on runs with 100+ agent profiles, even
though the simulation itself completed normally — the report would
silently fall back to "(no interview data)" since the failure is
caught as non-fatal by the caller.

180s doesn't leave enough headroom when each individual call can
legitimately take up to the LLM client's own per-call ceiling (600s
in llm_client.py, e.g. for claude-cli subprocess spawns). Raised to
match that ceiling.

Addresses self-review findings on the original three commits:

- Single CLI_CALL_TIMEOUT_SECONDS constant governs both providers and
  every timeout error message (the claude handler still said "300s"
  after the ceiling moved to 600; codex was left at 180 with no
  encoding handling — same cp1252 hazards the claude path already
  fixed).
- Interview batch budget now scales with the batch instead of a flat
  600s: one full per-call ceiling per interviewed agent. A fixed
  budget equals the worst case of a single call and still starves
  multi-agent batches.
- rapidfuzz override scoped with sys_platform == 'win32' marker so
  macOS/Linux keep the resolver's pick; pyproject now documents why
  the pin exists and when to drop it (the missing wheels are
  specifically Windows cp312, not all of win_amd64).
- Provider binaries resolved via shutil.which for a clear early error
  when the CLI is missing (and to tolerate npm-style .cmd shims on
  Windows).
- Warn when undecodable bytes were replaced with U+FFFD instead of
  corrupting output silently.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

The post-simulation interview panel was hardcoded to the 5 most active
agents, which (a) caps the qualitative evidence the final report can
draw on and (b) over-represents the vocal minority — silent agents,
who watched the whole simulation and engaged with none of it, often
hold the most informative objections.

- MIROFISH_INTERVIEW_MAX_AGENTS env var (default 5, preserving current
  behavior) controls panel size. Cost scales linearly: one LLM call per
  agent per platform; the batch timeout already scales with panel size.
- With activity data and a panel of >=6, selection becomes stratified:
  ~40% most vocal, ~30% LLM-picked for topical relevance from the
  remainder, ~30% silent non-participants sampled across the roster.
  Below the threshold the existing LLM selection path is unchanged.
- MIROFISH_INTERVIEW_EXTRA_QUESTIONS (';'-separated) appends fixed
  questions to the generated questionnaire — closed questions (price
  bands, adoption timing) whose answers can be coded into
  distributions for downstream quantitative use.

Tested: stratified selector unit-smoke (quotas, no duplicates, range,
classic path below threshold, no-LLM fallback) plus py_compile.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

…atified-interviews

…dedup

- _get_int_env helper: malformed MIROFISH_INTERVIEW_MAX_AGENTS values
  degrade to the default with a warning instead of crashing at import
  from the Config class body; floor of 1 guards zero/negative panels.
- Interview batches now run in chunks of 5 with a per-chunk timeout
  (ceiling x chunk size): the previous single all-or-nothing IPC
  transaction scaled to hours with large panels and lost every
  completed interview if one hung. A failed chunk now loses only
  itself; results accumulate across chunks.
- LLM-selected interview indices are deduped (order-preserving),
  coerced from strings and range-checked before use.

🤖 Generated with [Claude Code](https://claude.com/claude-code)