The router.SecureProvider interface previously required a public
IsSecure() bool method. Any test mock — or future production type —
could satisfy it by returning true, defeating the W1 "only wrapped
providers may flow past the boundary" contract through convention
rather than at the type level.
Replaces IsSecure() bool with an unexported security.Marker interface
that has a single secured() method. Go's method-set semantics key
unexported methods by their defining package, so only types declared in
internal/security can satisfy Marker. *SafeProvider gets the lone
secured() implementation; router.SecureProvider embeds Marker.
The seal forces every test mock that previously implemented IsSecure()
to either (a) be wrapped with security.WrapProvider(mp, nil) at the use
site, or (b) drop the method entirely if the mock never flows through
SecureProvider. 93 use sites across 11 test files were updated via a
per-package secureMock helper. WrapProvider with a nil firewall ref is
a no-op pass-through, so test behavior is unchanged.
Empirically: a type from outside internal/security can declare
`secured()` but the compiler will reject assigning it to
router.SecureProvider because the unexported method belongs to the
other package's namespace. Convention → compile-time guarantee.
Plan D from docs/superpowers/plans/2026-05-19-post-slm-unlock.md
(static portion; dynamic bandit-driven promotion deferred to D-2).
Routing previously let tier ordering (CLI > local > API) dominate
selection — Opus, in tier 3, would lose to a tier-1 CLI agent for
SecurityReview even though Opus is empirically stronger at that task.
This change introduces explicit per-arm overrides:
[[arms]]
id = "anthropic/claude-opus-4-7"
strengths = ["security_review", "planning"]
cost_weight = 0.3
Strengths gate cross-tier promotion: arms matching task.Type bypass
the tier loop and compete with each other directly. Promotion is a
preference, not a pin — if no strength-tagged arm is feasible
(backoff, pool capacity, tool support), selection falls through to
the default tier order.
CostWeight linearly dampens the cost penalty in scoreArm via
effectiveCost = 1 + CostWeight * (cost - 1)
CostWeight=1.0 (or unset) preserves current behavior; lower values
trade cheapness for quality. The earlier draft used cost^CostWeight
which inverts direction for sub-1 local-arm costs (raising a
fraction <1 to a fractional power makes it bigger, not smaller); a
monotonicity regression test prevents that drift.
- internal/router/arm.go: Strengths []TaskType, CostWeight float64,
HasStrength(), ResolvedCostWeight() (zero → 1.0).
- internal/router/selector.go: scoreArm strength bonus const
(strengthScoreBonus = 0.15) + linear cost dampening; selectBest
cross-tier promotion before tier loop.
- internal/router/router.go: ArmOverride type + ApplyArmOverrides()
returns unknown IDs; unknown strength names skipped with per-name
warning via slog.
- internal/router/task.go: ParseTaskTypeStrict() returns ok bool;
ParseTaskType now delegates so the two switches stay in sync.
- internal/config/config.go: ArmConfig + [[arms]] TOML wiring.
- cmd/gnoma/main.go: applies overrides after all initial arms
register; logs a warning when an [[arms]] id has no matching
registered arm.
Tests cover: predicate helpers, scoring direction across two arms,
linear-formula monotonicity on both sides of cost=1, cross-tier
promotion, empty-Strengths preserves tier order, promoted arm in
backoff falls through via full Router.Select path, observed-quality
tiebreak between two strength-tagged arms, ApplyArmOverrides happy
path + unknown-ID reporting + unknown-strength skipping.
- slm.Classifier: openaicompat → llamafile, 2s timeout + heuristic fallback,
heuristic baseline blended so Priority/RequiredEffort are never zeroed,
extractJSON strips markdown fences from small-model responses
- router.ParseTaskType: case-insensitive string → TaskType, unknown → TaskGeneration
- router.Arm.MaxComplexity: zero = no ceiling (preserves existing arm behavior);
filterFeasible excludes arms when task.ComplexityScore > MaxComplexity
- config.SLMSection: [slm] enabled / model_url / data_dir
- openaicompat.NewLlamafile: no API key, model = "default", no retries
- slm.Manager: DefaultDataDir() (XDG), Manifest() accessor
- cmd/gnoma: `gnoma slm setup` / `gnoma slm status` subcommands; SLM arm
registered with MaxComplexity=0.3 when enabled + set up
- tui: /config shows slm status (ready/missing/not set up + base URL if running)
- docs: roadmap updated to reflect llamafile pivot from Ollama
Adds explicit tier preference to arm selection so the router
deterministically prefers lower-cost arms before falling back:
tier 0: CLI agents (IsCLIAgent=true, subprocess/claude|gemini|vibe)
tier 1: local models (IsLocal=true, ollama/llamacpp)
tier 2: API providers (everything else)
Within a tier, quality/cost scoring still applies. filterFeasible still
gates on quality thresholds, so a low-quality local arm won't beat a
high-quality API arm when the task's minimum threshold rules it out.
Also adds Arm.Disabled: arms with Disabled=true are excluded from
auto-routing but remain selectable via ForceArm.
Implementation: armTier helper + selectBest refactored to try tiers in
order, bestScored picks within a tier. router.Select skips disabled arms
in allArms collection (forced arm bypasses disable check).
The discovery loop's reconcileArms removed the CLI-forced arm
(llamacpp/default) because the llama.cpp server reports the real model
name (e.g. gemma-26b), creating a mismatch. After 30s the forced arm
disappeared and all subsequent requests failed.
Three-layer fix:
- Eager: query the specific provider at startup to resolve the real
model name before registering the forced arm
- Lazy: reconcileArms detects placeholder "default" arm names and
atomically renames them when discovery reveals the real identity,
with an onReconcile callback to update the session and TUI
- Guard: the forced arm is never garbage-collected by the removal loop
Also fixes misleading /init error messaging — failed inits now show
"loaded from disk (init failed)" instead of "AGENTS.md written to".
provider/openai:
- Fix doubled tool call args (argsComplete flag): Ollama sends complete
args in the first streaming chunk then repeats them as delta, causing
doubled JSON and 400 errors in elfs
- Handle fs: prefix (gemma4 uses fs:grep instead of fs.grep)
- Add Reasoning field support for Ollama thinking output
cmd/gnoma:
- Early TTY detection so logger is created with correct destination
before any component gets a reference to it (fixes slog WARN bleed
into TUI textarea)
permission:
- Exempt spawn_elfs and agent tools from safety scanner: elf prompt
text may legitimately mention .env/.ssh/credentials patterns and
should not be blocked
tui/app:
- /init retry chain: no-tool-calls → spawn_elfs nudge → write nudge
(ask for plain text output) → TUI fallback write from streamBuf
- looksLikeAgentsMD + extractMarkdownDoc: validate and clean fallback
content before writing (reject refusals, strip narrative preambles)
- Collapse thinking output to 3 lines; ctrl+o to expand (live stream
and committed messages)
- Stream-level filter for model pseudo-tool-call blocks: suppresses
<<tool_code>>...</tool_code>> and <<function_call>>...<tool_call|>
from entering streamBuf across chunk boundaries
- sanitizeAssistantText regex covers both block formats
- Reset streamFilterClose at every turn start
vikingowl