The system prompt skill index truncates long descriptions to 57 chars,
but this limit was a hardcoded magic number. Extract it as a named
constant and factor the normalization logic into a shared private
helper so the extraction function and the new truncation predicate
cannot drift.
No behaviour change — pure refactor.
Follow-up on the salvaged commit: the xurl skill loads even when x_search
isn't registered (check_fn-gated on xAI credentials), so per the
cross-toolset reference rule the skill must not name it. Replaced the
skill's x_search routing block and workflow step with skill-native
wording (raw engageable posts, authenticated context, write-evidence
rule). The cross-surface comparison stays on surfaces where both are
known to exist: x-search feature docs, toolset description, tools-config
setup note, and the x_search tool schema (kept generic, no tool names).
Rewrote the routing tests to pin the placement contract, including that
the skill never names credential-gated surfaces.
Make the x_search / xurl boundary explicit in the skill, feature docs,
toolset metadata, setup note, and reference pages, while keeping the
model-facing x_search schema generic (no static xurl name).
Regression tests assert behavioral routing invariants rather than frozen
prose snapshots. Drop the stale CI-only plugin/hangup hunks already on
main so this rebases cleanly.
skills/computer-use/SKILL.md sat at the root of the bundled skills
tree as an uncategorized single-skill directory. Move it to
skills/autonomous-ai-agents/computer-use/ alongside claude-code,
codex, opencode, and hermes-agent.
- git mv skills/computer-use -> skills/autonomous-ai-agents/computer-use
(history preserved)
- root-level-skill docstring examples (commands.py,
generate-skill-docs.py) now use a generic placeholder instead of
naming a specific skill, since categorizing root-level skills is
ongoing
- docs: move the bundled page to autonomous-ai-agents-computer-use,
update sidebars.ts, skills-catalog.md, and the two skill-path
references in features/computer-use.md
E2E validated: fresh sync_skills() copies to the new nested path;
existing installs with the old flat copy keep it untouched (manifest
name-keyed, hash unchanged -> skipped, no duplicate);
_get_category_from_path resolves 'autonomous-ai-agents'; docusaurus
build green; 396 targeted tests pass.
The 'queues an Enter-submitted draft while compaction is active' test
pastes a large message to push the session over the fixture's 22k
threshold_tokens. At repeat(500) the payload is only ~4k tokens — the
other ~18k came from the ambient system prompt (tool schemas + skills
index + memory), leaving the trigger margin-less. The hermes-agent
skill hub restructure (e3d524b482) shrank the bundled skills index by
~160 tokens and dropped the total just under threshold: compaction never
started, waitForHeldCompletion() hung, and the test timed out at 90s on
every branch since — including main (first red run: 9a4d1a0130).
Bump the payload to repeat(1500) (~12.4k tokens, total ~30k) so the
test crosses the threshold on its own weight with ~8k tokens of margin,
and document the invariant so the next prompt-weight change doesn't
resurrect this.
A direct run (cronjob action='run' / webhook-triggered manual fire) takes
the job's fire_claim and advances next_run_at. When it races the external
provider's scheduled fire for the same occurrence, Chronos loses the claim
and — by design — does not re-arm (the winner owns the re-arm). But the
direct-run winner never notified the provider, so the NAS one-shot for the
consumed occurrence was left stale forever and the recurring job silently
stopped firing.
Observed in production: a managed 1-minute review job stalled for 20 hours
because a GitHub-webhook direct run claimed the job 2s before the Chronos
fire arrived; every subsequent occurrence was orphaned while /api/status
stayed green.
Fix: after a *claimed* direct execution completes (success or failure —
next_run_at advances at claim time either way), call
_notify_provider_jobs_changed_safe() so the active provider re-arms the
post-run next_run_at. No-op for the built-in ticker; claim-lost direct
runs still never notify (the winning scheduler owns the re-arm).
Claude Code's /simplify was renamed away, community-restored, and rebuilt
since our 3-agent port (v2.1.63 -> v2.1.154+). This brings simplify-code
in line with the current upstream design, re-expressed in our own wording
and layered onto our existing risk-tier/confidence machinery:
- New Reviewer 4 (Altitude): flags band-aid fixes layered on shared
infrastructure — special cases, symptom patches with unfixed sibling
sites, workaround stacks — and points at the deeper mechanism fix.
Matches our AGENTS.md 'fix the class, not the site' rubric.
- Explicit cleanup-vs-bug-hunt boundary: this skill improves working
code; correctness review stays with requesting-code-review.
- Inline single-pass fallback when delegate_task is unavailable (leaf
subagents, delegation disabled) — previously the skill just broke;
now all four angles run sequentially with honest disclosure.
- Finding format gains a concrete-cost field.
- Efficiency reviewer adds closure-capture scope-retention leaks.
- Pitfalls: fan-out cap 3 -> 4, band-aid-vs-deliberate-boundary caveat,
no-bug-hunting drift guard.
Kept our value-adds upstream lacks: SAFE/CAREFUL/RISKY tiers,
Chesterton's Fence via git blame, dry-run/focus/scope modifiers.
With the built-in x_search tool (xAI-backed, returns a synthesized
answer) now shipping, the xurl skill's plain 'searching posts' wording
no longer disambiguated the two search surfaces. Describe xurl search
in its own terms — authenticated X index query returning raw post JSON
with IDs suitable for immediate engagement — so agents route correctly
whether or not x_search is enabled. No cross-toolset reference added,
per the schema/skill cross-reference rule.
Add two pitfalls discovered when running the skill against a fresh
Jupyter server:
- Pitfall #9: When the websocket reply channel hangs on every execute
even though the kernel actually ran (REST shows execution_state=idle
and execution_count increments), force zmq transport with
--transport zmq. The zmq transport uses jupyter_client directly and
sidesteps the broken websocket layer.
- Pitfall #10: A fresh ServerApp rejects POST /api/sessions with
"_xsrf argument missing from POST" unless you start it with
--ServerApp.disable_check_xsrf=True. Needed for REST-only flows
where no browser/cookie is establishing the XSRF token.
The yuanbao skill is platform-specific guidance for Tencent Yuanbao
group chats — niche for the default bundled set. Per the
'when in doubt, optional' rule, ship it as an optional skill.
Install via: hermes skills install official/yuanbao/yuanbao
The nano-pdf description repeated the skill name ('via nano-pdf CLI'),
violating the skill authoring standard, and spent its char budget
restating the name instead of distinguishing the skill from the
structural pdf skill. New description: 'Edit text in existing PDFs via
natural-language prompts.' (56 chars) — no name repetition, and
'text in existing PDFs' contrasts cleanly with pdf's
'Create, merge, split, fill, and secure PDF files.'
Propagated to the auto-generated docs pages and zh-Hans locale
(catalog row + per-skill page) for locale parity.
Per the 'when in doubt, optional' rule — zero-shot image segmentation
via SAM is a niche computer-vision capability with heavy deps
(torch, transformers), not something most users load.
Install via: hermes skills install official/mlops/segment-anything
Both are heavy-dep, GPU-bound local music-generation skills (8-16GB VRAM,
torch stacks, manual source patches) that were bundled in two different
categories (media/ and mlops/models/). Per the 'when in doubt, optional'
rule they now sit side by side in optional-skills/creative/, and the
bundled songwriting-and-ai-music skill points at them for local generation.
- git mv skills/media/heartmula -> optional-skills/creative/heartmula
- git mv skills/mlops/models/audiocraft -> optional-skills/creative/audiocraft
- cross-link related_skills both ways + songwriting-and-ai-music
- new section 10 in songwriting-and-ai-music with install commands
- docs: bundled pages -> optional pages (en + zh-Hans), catalogs, sidebar
Install via:
hermes skills install official/creative/heartmula
hermes skills install official/creative/audiocraft
The hermes-agent skill body was a 51KB monolith loaded in full on every
trigger. It is now a lightweight hub (~12KB): identity, quick start, the
tmux orchestration guide (kept in-body — the autonomous-ai-agents category
contract), surface orientation, and hard invariants, with a routing table
into 18 reference files that carry the depth.
Absorbed four Hermes-specific skills as first-class references so their
content gains a discoverable home and room to grow without bloating any
primary body:
- skills/hermes-themes -> references/themes.md + templates/skin.yaml
- skills/hermes-desktop-plugins -> references/desktop-plugins.md + templates/plugin.js
- skills/productivity/tui-widgets -> references/tui-widgets.md + templates/clock.mjs
- skills/productivity/petdex -> references/petdex.md
New references extracted from the old body: cli-reference, slash-commands,
providers-and-models, configuration, project-context-files,
security-privacy, background-systems, windows-quirks, troubleshooting,
contributor-guide (native-mcp and webhooks already existed). Also commits
delegate-task-concurrency-diagnosis and portal-auth-for-third-party-apps,
which the old body referenced but the repo never shipped.
Description updated to cover the widened scope:
'Use, configure, theme, extend, and orchestrate Hermes Agent.' (60 chars)
skills/dogfood/SKILL.md sat at the root of the bundled skills tree,
making it one of the few uncategorized skills (Discord /skill
autocomplete listed it under 'uncategorized'; hermes_cli/commands.py
cited it as the example). Move it to
skills/software-development/dogfood/ alongside the other QA/testing
skills (test-driven-development, systematic-debugging,
requesting-code-review).
- git mv skills/dogfood -> skills/software-development/dogfood
(history preserved)
- fix test fixture path in tests/tools/test_browser_console.py
- update root-level-skill docstring examples (commands.py,
generate-skill-docs.py) to cite computer-use, which is still root-level
- drop 'dogfood' from the category list in hermes-agent-skill-authoring
SKILL.md (en + zh-Hans docs mirrors)
- docs: regenerate/move the bundled page to
software-development-dogfood (en + zh-Hans), update sidebars.ts,
skills-catalog.md, and the adversarial-ux-test related-skills link
E2E validated: fresh sync_skills() copies to the new nested path;
existing installs with the old flat copy keep it untouched (manifest is
name-keyed, hash unchanged -> skipped, no duplicate);
_get_category_from_path resolves 'software-development'; docusaurus
build green.
Maintainer scoping decision for the #51226 salvage: document that
select_context() is for engines that must REPLACE per-request context
(retrieval/routing) — pre_llm_call is inject-only by documented cache
design; that observation-only plugins should implement a MemoryProvider
(sync_turn) rather than a context engine, with on_turn_complete scoped
as the observation mirror for engines that already select; and that a
non-no-op select_context naturally changes the prompt-cache prefix on
turns where the selection changes — engines should return stable
selections when nothing changed.
Verification follow-up for the #51226 salvage: the host call site guarded
select_context with hasattr(), but the ABC defines a default on every
engine, so the built-in ContextCompressor (and any non-implementing
engine) still paid per-request shallow copies of the conversation
history plus a hook call on every provider request. Identity-check the
bound method against ContextEngine.select_context and return the
request untouched — mirroring the existing base-method short-circuit in
_notify_context_engine_turn_complete — so the default path does zero
work, not just produces an identical result.
Adds two pins: the base no-op is never invoked (patched-to-raise base
stays silent), and ContextCompressor.__dict__ contains neither new verb.
Also registers the contributor email mapping for @chaos-xxl.
- website guide: on_turn_complete() now carries the same best-effort coverage
caveat as the ABC docstring (fires from the finalization seam; abnormal
early-return paths bypass it) — removes the doc/code inconsistency.
- test: finalization seam emits on_turn_complete with usage=None + the
interrupted flag for an interrupted finalized turn. Docstring records that
the negative early-return-bypass half is best-effort and deferred to a
shared-seam follow-up rather than pinned via a full run_conversation harness.
Addresses the hermes-sweeper review on #51226:
- _apply_context_engine_selection now passes shallow copies of the read-only
conversation_messages / incoming_message to the hook, so an engine mutating
them in place cannot corrupt persisted transcript state (enforces the
request-only contract, not just documents it). Adds a mutation-regression
test asserting persisted history + incoming message are untouched.
- on_turn_complete docstring: scope the coverage claim to the standard
finalization seam. Some abnormal early-return paths (content-policy block,
provider terminal failure) currently persist+return without finalization and
don't emit the hook; documented as best-effort with a shared-seam follow-up,
rather than over-promising a guaranteed callback for every early exit.
- _apply_context_engine_selection: reject an empty list. all([]) is True, so
a [] returned by a failing/buggy engine previously replaced a valid request
with an empty message list the downstream sanitizers can't restore; now it
falls open to the unmodified request (honors the fail-open contract).
Thanks @johnnykor82 for catching this on #41918's review.
- test: empty list keeps the original request (fail-open regression).
- docs: document select_context()/on_turn_complete() in the public
context-engine plugin guide (were still describing only the old contract).
- context_engine.py: document that select_context() runs before cache-control
and all request sanitizers, so (a) replacements still pass host validation
and (b) the no-op default keeps the request byte-stable (AGENTS.md prompt-
cache invariant). Note the hook is evaluated per provider request.
- tests: no-op path is byte-stable for cache-control; a role-unusual
replacement is passed through for the existing downstream sanitizers to
normalize (select_context does structural validation only).
The on_turn_complete() observation hook is the engine's post-turn signal,
so it should receive the completed turn's canonical token usage when the
host has it, not a hardcoded None. Per @johnnykor82's #41918 contract: the
engine uses prompt/completion + cache_read/write/reasoning buckets to judge
how large/expensive the selected context was before the next select_context().
- conversation_loop.py: stash the most recent provider response's usage_dict
(the same canonical shape fed to update_from_response) on the agent as
_last_turn_usage; reset to None at turn start so turns that never reach a
provider response (early failure / interrupt) forward None, not a stale
prior turn's usage.
- turn_finalizer.py: forward agent._last_turn_usage instead of usage=None.
- context_engine.py: document the usage param contract on the ABC hook.
- tests: cover both ends through the real finalize_turn path — completed turn
forwards the full canonical bucket set intact; no-response turn forwards None.
Co-authored-by: johnnykor82 <johnnykor82@users.noreply.github.com>
Adds the post-turn observation verb as the companion to select_context():
an optional, no-op-default on_turn_complete() called once after the
assistant/tool loop finishes, with the finalized transcript snapshot. Lets
an engine ingest/index/summarize the completed turn to inform the next
select_context(). Wired via _notify_context_engine_turn_complete() from
turn_finalizer.finalize_turn(); fail-open, base no-op short-circuited so
non-implementing engines (incl. the built-in compressor) pay nothing.
This is the request-assembly + observation pair from #41918; with this
commit the PR fully subsumes #41918's two hooks (prepare_request_messages
-> select_context, on_turn_complete) rather than only the selection half.
Co-authored-by: johnnykor82 <johnnykor82@users.noreply.github.com>
Adds an optional, no-op-default select_context() hook to the ContextEngine
ABC, called every turn after the request messages are assembled and before
provider dispatch — independent of should_compress(). Lets an engine select
or replace which context enters the prompt for a single request (retrieval,
topic routing, role/branch switching) without mutating persisted history,
removing the need to abuse should_compress()=True as a per-turn callback.
The host call site (_apply_context_engine_selection) is fail-open: a missing
hook, an exception, or an invalid return value leaves the assembled request
untouched. Additive and non-breaking: the built-in compressor and every
existing engine are unaffected.
Consolidates the per-turn request-assembly surface proposed across #41918,
Related: #36765#41918#24949#47109#50053#23837#25115#29370
Routine automatic compression stays silent-by-design on chat platforms
(default unchanged, byte-identical). New opt-in config key
compression.progress_notices (bool, default false) opens a gate on the
gateway noise filter (_prepare_gateway_status_message) that lets ROUTINE
compression progress statuses through to chat surfaces.
- Membership is derived from the #69550 status template constants in
agent/conversation_compression.py (compiled to a literal-escaped regex,
never re-inlined wording), so unrelated noisy statuses (aux failures,
provider retry/rate-limit chatter) stay suppressed even when enabled.
- The compaction completion notice (COMPACTION_DONE_STATUS, #69546
lifecycle 'compacted' edge) already flows through the status path and
passes the filter — no new emit site needed.
- Config wired everywhere: hermes_cli/config.py DEFAULT_CONFIG,
cli-config.yaml.example, gateway raw-YAML read (live, mtime-cached),
gateway hot-reload cache-busting key list, website configuration docs.
- Failure notices and manual /compress feedback remain always-visible;
VISIBLE_COMPRESSION_MESSAGES and emit sites untouched.
Design by @havok-training (issue #52995).
_render_tool_calls only handled dict-shaped entries; a SimpleNamespace-
shaped tool_call (SDK-style stream-stitched responses) rendered as
'[called tool: tool]', silently losing the function name and arguments
from the advisory view. Handle both shapes (including a namespace-shaped
nested function inside a dict entry).
One-hunk hardening salvaged from closed#59712.
Co-authored-by: SquabbyZ <601709253@qq.com>
Reference models may have a smaller context window than the aggregator
(e.g. kimi-k2.7-code @ 262K advising a glm-5.2 @ 1M conversation).
Without context-length protection, a reference whose window is exceeded
gets a hard HTTP 400 from the provider, which _run_reference's
try/except silently converts to a [failed: …] note — the MoA turn
silently degrades to fewer references (#60345).
Redesigned implementation of #60387:
- Estimate AFTER the advisory system prompt is prepended, so the
request that is actually sent is what gets budgeted.
- Reserve output headroom: the preset's reference_max_tokens when set,
else an 8192-token constant, plus a 10% estimator-error fraction.
- Trim on advisory-view boundaries (text-only user/assistant turns; no
tool-result frames to orphan), preserving the system prompt, the
user-first invariant after every pop (never assistant-first), and the
trailing synthetic user turn.
- Cache get_model_context_length per (provider, model) in a per-fan-out
dict shared across the worker threads, so a turn resolves each
window once instead of probing metadata sources
per-reference-per-iteration (failures are cached too).
Co-authored-by: webtecnica <75556242+webtecnica@users.noreply.github.com>
Follow-ups for salvaged #56344:
- A reference that completes between the interrupt check and the reap
keeps its REAL output and accounting (the provider call billed) instead
of being zeroed with a placeholder.
- A reference still in flight at interrupt time gets a placeholder in the
results, but its future now carries a done-callback that folds the
eventual real usage/cost into the facade's pending accounting
(late_accounting_sink -> _record_late_reference_accounting), so billed
spend is never silently dropped. Pending totals are folded (not
overwritten) and guarded by a lock since done-callbacks fire on
executor worker threads.
- Interrupted placeholder results are no longer written into the facade's
turn-scoped reference cache: a cache HIT never re-runs references, so
caching a partial snapshot would replay '[skipped: interrupted by
user]' notes for the rest of the turn. The cache is left empty and the
next create() re-runs the fan-out.
agent/tool_executor.py's concurrent tool batch checks agent._interrupt_requested
and aborts the wait early; agent/moa_loop.py's _run_references_parallel had
no equivalent, so a MoA-enabled turn blocked on ThreadPoolExecutor.result()
until every reference model finished or hit its own individual
auxiliary.moa_reference timeout -- there was no way for the user to abort a
live turn mid-fanout.
Thread an optional `agent` parameter through aggregate_moa_context ->
_run_references_parallel (used when MoA references run alongside the main
model) and MoAClient/MoAChatCompletions (used when the MoA preset itself is
the acting model), then poll concurrent.futures.wait() in
_REFERENCE_POLL_INTERVAL_S slices instead of blocking on future.result() per
reference, checking agent._interrupt_requested each cycle.
Deliberately scoped to interrupt/cancel only -- no new or changed timeout
value, so this doesn't overlap open PRs #53784/#53875 (which lower the
per-reference timeout default but don't add interrupt support). `agent` is
optional and defaults to None, so any caller that doesn't pass it keeps
today's uninterruptible blocking behavior unchanged.
Extends the all-references-failed short-circuit (#56975) to the
persistent `provider: moa` facade path: MoAChatCompletions.create()
previously attached 'use the reference responses below' guidance built
entirely from failure sentinels and called the aggregator with it. Now
an all-failed turn attaches either the sanitized unavailability notice
(loud policy) or nothing (silent policy), and the aggregator — which IS
the acting model — simply acts alone. Advisor accounting for the failed
fan-out is still recorded.
Co-authored-by: liuhao1024 <sunsky.lau@gmail.com>
When every MoA reference model returns a failure (HTTP error, timeout,
etc.) or is skipped by the recursion guard, the one-shot aggregator
synthesis call is now skipped entirely. Previously it would try to
synthesise a wall of failure sentinels, which could block for the full
provider timeout (observed ~6 min on SenseNova) before returning a
non-retryable error that left the session hanging.
The early return carries the sanitized unavailability notice (never raw
provider error text, per the failed-reference containment) so the main
agent loop can still act in single-model mode.
Salvaged from #56975, reworked atop the _is_failed_reference helpers.
Follow-ups for salvaged #53784:
- reference_timeout now defaults to None = no per-preset override, so the
reference fan-out inherits auxiliary.moa_reference.timeout (900s default)
via call_llm's own per-task timeout resolution. The PR's 30.0s default
would have cut off long-thinking advisors mid-response, and its 300s max
cap capped legitimate explicit values — both removed. Explicit per-preset
values are still honored as-is.
- _is_failed_reference also treats '[skipped: …]' recursion-guard notes as
internal sentinels, keeping them out of both aggregator prompts.
- Dashboard/desktop TS types updated to number | null; web_server validator
accepts null/empty as 'inherit'.
Keep MoA reference display events off the machine-readable -Q stdout
surface (platform=cli with tool_progress_mode=off) while preserving them
everywhere else. Extracts the relay into module-level helpers so the
policy is testable.
Salvaged from #67334.
Per the 'when in doubt, optional' rule — the live-kernel workflow needs
uv + JupyterLab + a running server + a cloned hamelnb repo, a niche
setup that shouldn't ship active by default.
Renamed jupyter-live-kernel -> jupyter-notebook (skill name, page slugs,
catalogs, sidebar, zh-Hans mirror, darwinian-evolver related_skills).
Install via: hermes skills install official/data-science/jupyter-notebook
Follow-up to the #67690 salvage (@m4r13y). The PR's tools/env_probe.py
hunk was written against the old capture_output=True _run(); #67964/#67999
rewrote _run to temp-file capture on July 20, so that hunk no longer
applied — but the rewritten _run still lacked creationflags and kept
flashing one console per probe (~5 per kanban worker start) from
windowless parents. Re-implement the one-line fix against the current
shape: creationflags=windows_hide_flags() on the temp-file subprocess.run,
preserving the #67964 grandchild-can't-wedge-the-pipe contract.
Also add the tests the PR didn't ship, in
tests/test_windows_subprocess_no_window_flags.py:
- env_probe._run passes CREATE_NO_WINDOW and keeps temp-file (non-PIPE)
stdout/stderr + DEVNULL stdin
- lazy_deps uv install / pip --version probe / pip install fallback /
ensurepip bootstrap all pass CREATE_NO_WINDOW
- suppress_platform_ver_console: POSIX no-op (platform._syscmd_ver
untouched, win32_ver() still returns), and simulated-Windows stubbing
(echo stub installed, idempotent, never raises)
From windowless processes (the pythonw gateway and the kanban workers it
spawns), three spawn paths flash visible console windows on Windows:
1. tools/env_probe.py::_run() ran its interpreter/pip probes
(python3 / python / pip / 'python3 -m pip' / PEP-668 check, ~5 per
worker start) without creationflags — one console flash per probe.
2. tools/lazy_deps.py had four spawn sites with the same defect:
'uv pip install', the 'pip --version' probe, ensurepip, and the
pip install fallback.
Both now pass creationflags=windows_hide_flags() (CREATE_NO_WINDOW on
Windows, 0 on POSIX) — stdio capture still works because the child is
hidden, not detached.
3. CPython 3.11's platform.win32_ver() unconditionally calls
_syscmd_ver(), which runs 'cmd /c ver' via
subprocess.check_output(shell=True) with no window suppression. Any
dependency touching platform.uname()/version()/platform() at import
time flashes one 'cmd' window per windowless process. New helper
_subprocess_compat.suppress_platform_ver_console() (Windows-only,
never raises) stubs platform._syscmd_ver so win32_ver() falls back to
sys.getwindowsversion().platform_version — verified byte-identical
platform.platform() output on CPython 3.11
('Windows-10-10.0.26100-SP0' either way). Called at the top of
hermes_cli/main.py, right after the hermes_bootstrap guard, before
heavyweight imports.
Verified on Windows 11 by polling EnumWindows at ~15 ms and attributing
new visible HWNDs to the suspect process tree (conhost child presence is
NOT evidence of a visible window — it appears even with
CREATE_NO_WINDOW). Tests: tests/tools/test_windows_native_support.py,
test_env_probe.py, test_lazy_deps.py, test_lazy_deps_durable_target.py —
153 passed; the 3 failures are pre-existing on upstream/main in a
Windows environment (POSIX-only assertions and NTFS chmod semantics).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The per-advisor enabled toggle adds enabled=True to normalized slots;
the JSON-string-parse and per-slot max_tokens tests from sibling
clusters asserted exact dicts. Compare against the enabled-augmented
expectation instead.
The per-reference-model enabled toggle (#59753 salvage) intentionally adds
'enabled' to normalized slot dicts. The two endpoint tests asserted the
exact key set {provider, model} — convert them to subset + round-trip
contracts so optional slot keys (enabled, reasoning_effort, max_tokens)
don't break them again.
Follow-up for salvaged PR #59753 rebased over the per-slot
reasoning_effort feature: _clean_slot now round-trips reasoning_effort
AND enabled together; add a normalize→normalize regression test, update
the validate/normalize agreement contract for the canonical enabled
default, restore the desktop per-slot toggle test on the current
autosave editor, and map oppenheimor's contributor email.
