PluginLlm's four public entry points share _gate/_finish/_host_kwargs; drop
dead classify_failure_scope/_REASON_SCOPES (and their tests) and unify the
three _norm_* helpers; should_skip_candidate routes through a scope predicate
table. Injected caller kwargs, audit dicts and log lines unchanged.
Most providers are models: they ask Hermes to run a tool and Hermes runs it,
so the transcript and the loop's counters see every tool iteration. Some
providers are agents — an ACP CLI behind a client shim, or the codex
app-server, which already takes an analogous path in `agent/codex_runtime.py`.
They execute their own read/edit/execute tools inside their own session, and
by the time Hermes sees the response that work is done.
Those calls must never come back as pending `tool_calls` — Hermes would
re-run finished work. But summarising them into `reasoning` blinds two
subsystems:
- the self-improvement loop, which distils memories and skills by replaying
`messages`; a one-line activity feed teaches it nothing;
- the skill-review nudge, whose `_iters_since_skill` counter only moves on
Hermes tool iterations, of which there are none.
So a client may hand both back on the completion object —
`hermes_projected_messages` (completed assistant(tool_calls) + tool(result)
rows) and `hermes_provider_tool_iterations` — and
`splice_provider_projection` applies them. Rows go through `append_message`
like every other live-transcript append, so they carry a timestamp and
persist the same way the codex projection path's rows do.
The splice is append-only, sits before this turn's assistant message so the
order reads call -> result -> answer, and is a no-op for every client that
sets neither attribute, i.e. every ordinary OpenAI-compatible provider.
Garbage attribute values are tolerated rather than allowed to break the turn.
