chore(pstack): route solo defaults to Fable 5.1

Co-authored-by: lauren <poteto@users.noreply.github.com>

Author: cursoragent

pstack/.cursor-plugin/plugin.json

@@ -1,7 +1,7 @@
 {
 	"name": "pstack",
 	"displayName": "pstack",
-	"version": "0.14.5",
+	"version": "0.14.6",
 	"description": "if you want to go fast, go deep first. pstack helps you write less, but higher quality code. rigorous agent workflows you can parallelize with confidence.",
 	"author": {
 		"name": "Lauren Tan"

pstack/README.md

@@ -27,7 +27,7 @@ two steps:
 
 new here? the [pstack guide](./docs/guide/README.md) walks you through a first real task, from setup and prompting through verification and overnight runs.
 
-that's it. the other skills are situational; the mode skill uses them for you as needed. out of the box the mode splits work by model strength: precisely-specified code goes to sol, fast mechanical code goes to grok, and prose and judgment go to fable. the default panel is fable / sol / grok / opus 5. [`/setup-pstack`](./skills/setup-pstack/SKILL.md) changes any of it.
+that's it. the other skills are situational; the mode skill uses them for you as needed. out of the box the mode splits work by model strength: precisely-specified code, prose, and judgment go to fable 5.1, while fast mechanical code goes to grok. the default panel is fable 5.1 / sol / grok / opus 5. [`/setup-pstack`](./skills/setup-pstack/SKILL.md) changes any of it.
 
 ## usage
 

pstack/skills/architect/SKILL.md

@@ -30,7 +30,7 @@ Skip Phase A only when the work is genuinely greenfield with no surrounding syst
 
 Run the **arena** skill with the design-sketch task and the Phase A grounding artifacts. Pass `references/runner-prompt.md` as each runner's prompt. Each candidate produces a design package shaped per `references/rationale-template.md`: the caller's usage written first, then the type sketch, function signatures, module map, and prose rationale derived from it.
 
-Use your configured architect runners (defaults `claude-fable-5-thinking-max`, `gpt-5.6-sol-max`, `grok-4.6-fast-xhigh`, `claude-opus-5-thinking-xhigh`).
+Use your configured architect runners (defaults `claude-fable-5-1-thinking-max`, `gpt-5.6-sol-max`, `grok-4.6-fast-xhigh`, `claude-opus-5-thinking-xhigh`).
 
 Design it twice. Require at least two structurally distinct candidates before synthesis, even when the first looks sufficient. This is the **exhaust-the-design-space** principle skill made concrete. Whole-shape alternatives, not point fixes inside one shape.
 

pstack/skills/arena/SKILL.md

@@ -25,7 +25,7 @@ The N candidates will receive the same prompt, so the prompt is the contract. Ge
 
 1. State the artifact each candidate is producing.
 2. Derive the rubric. State what success looks like for *this* task, then turn it into 3-6 concrete gradeable criteria. Concrete: `Adds a --dry-run flag that skips writes`. Vague: `code is correct`. The rubric is the picker's tool in Phase D; candidates only see the task.
-3. Pick the runners. Use `arena runners` from `~/.cursor/rules/pstack-models.mdc` when present. Otherwise default to one each on `claude-fable-5-thinking-max`, `gpt-5.6-sol-max`, `grok-4.6-fast-xhigh`, `claude-opus-5-thinking-xhigh`. Spawn more when the arena covers multiple design directions. Same model N times when the work is generation-bound rather than judgment-sensitive.
+3. Pick the runners. Use `arena runners` from `~/.cursor/rules/pstack-models.mdc` when present. Otherwise default to one each on `claude-fable-5-1-thinking-max`, `gpt-5.6-sol-max`, `grok-4.6-fast-xhigh`, `claude-opus-5-thinking-xhigh`. Spawn more when the arena covers multiple design directions. Same model N times when the work is generation-bound rather than judgment-sensitive.
 4. Assign output paths. Each candidate writes to its own location (a git worktree where possible, otherwise `/tmp/arena-<slug>/candidate-<n>/`). N candidates writing to the same path is shared mutable state and fails the the **separate-before-serializing-shared-state** principle skill test.
 
 ## Phase B: Fan out
@@ -38,7 +38,7 @@ If a candidate fails to produce output, proceed with N-1 and note the dropout in
 
 ## Phase C: Cross-judge
 
-After all Phase B candidates complete, choose one model from the `arena cross-judge pool` in `~/.cursor/rules/pstack-models.mdc` when present. Otherwise use `claude-fable-5-thinking-max`, `gpt-5.6-sol-max`, `grok-4.6-fast-xhigh`, `claude-opus-5-thinking-xhigh`. Prefer a different model family from the parent's. Spawn one readonly judge subagent on that model. It sees the rubric and the candidates by path label, scores each criterion, and recommends a base with rationale. It runs in parallel with the parent's reading in Phase D, not with the candidates themselves. Spawning while candidates are still writing means the judge sees partial or empty outputs and reports them as dropouts.
+After all Phase B candidates complete, choose one model from the `arena cross-judge pool` in `~/.cursor/rules/pstack-models.mdc` when present. Otherwise use `claude-fable-5-1-thinking-max`, `gpt-5.6-sol-max`, `grok-4.6-fast-xhigh`, `claude-opus-5-thinking-xhigh`. Prefer a different model family from the parent's. Spawn one readonly judge subagent on that model. It sees the rubric and the candidates by path label, scores each criterion, and recommends a base with rationale. It runs in parallel with the parent's reading in Phase D, not with the candidates themselves. Spawning while candidates are still writing means the judge sees partial or empty outputs and reports them as dropouts.
 
 ## Phase D: Pick a base
 

pstack/skills/how/SKILL.md

@@ -65,7 +65,7 @@ Then proceed to Step 3.
 Spawn a single Task subagent that explores and explains in one pass:
 
 - `subagent_type`: `generalPurpose`
-- `model`: your configured how-explainer model (default `claude-fable-5-thinking-max`)
+- `model`: your configured how-explainer model (default `claude-fable-5-1-thinking-max`)
 - `readonly`: `true`
 
 The agent does its own exploration (Glob, Grep, Read) and writes the explanation directly. Read `references/explainer-prompt.md` for the communication style and output format. Same structure, just no explorer findings as input.
@@ -77,7 +77,7 @@ Proceed to Step 4.
 Once all explorers return, spawn a single Task subagent to synthesize their findings into one coherent explanation:
 
 - `subagent_type`: `generalPurpose`
-- `model`: your configured how-explainer model (default `claude-fable-5-thinking-max`)
+- `model`: your configured how-explainer model (default `claude-fable-5-1-thinking-max`)
 - `readonly`: `true`
 
 The explainer gets all explorers' findings and writes the human-facing explanation (output format below). Read `references/explainer-prompt.md` for the full prompt template. The explainer reconciles overlapping findings, resolves contradictions, and weaves the slices into a unified picture.
@@ -110,7 +110,7 @@ Run the full explain flow above (Steps 1-4). You must understand the architectur
 
 ### Step 2. Spawn Critics
 
-After the explanation is complete, spawn one architectural critic per model in your configured how-critics list (defaults `claude-fable-5-thinking-max`, `gpt-5.6-sol-max`, `grok-4.6-fast-xhigh`, `claude-opus-5-thinking-xhigh`), all in a single message.
+After the explanation is complete, spawn one architectural critic per model in your configured how-critics list (defaults `claude-fable-5-1-thinking-max`, `gpt-5.6-sol-max`, `grok-4.6-fast-xhigh`, `claude-opus-5-thinking-xhigh`), all in a single message.
 
 For each critic:
 - `subagent_type`: `generalPurpose`

pstack/skills/interrogate/SKILL.md

@@ -37,7 +37,7 @@ Launch all reviewers in a single message using the Task tool. Use the `interroga
 
 | Subagent | Default model |
 |----------|---------------|
-| Reviewer A | `claude-fable-5-thinking-max` |
+| Reviewer A | `claude-fable-5-1-thinking-max` |
 | Reviewer B | `gpt-5.6-sol-max` |
 | Reviewer C | `grok-4.6-fast-xhigh` |
 | Reviewer D | `claude-opus-5-thinking-xhigh` |

pstack/skills/poteto-mode/SKILL.md

@@ -88,7 +88,7 @@ Read the leaf skill in full for any principle you apply. Each entry names when i
 
 **Use `subagent_type: "poteto-agent"` for any subagent you spawn inside a playbook step** (code-writing delegates, ad-hoc helpers). `/poteto-mode` and `poteto-agent` route through the same wrapper. Routed workflow skills (`how`, `why`, `interrogate`, `reflect`, `swarm`) set their own `subagent_type` for diverse-model review; respect what the skill prescribes, don't override to `poteto-agent`.
 
-**Defaults for every `Task` call.** `run_in_background: true`, agent mode (readonly strips MCP), file pointers not inlined context, explicit model per role (configurable via `/setup-pstack`; defaults `grok-4.6-fast-xhigh` for code, `claude-fable-5-thinking-max` for prose and judgment). Code delegates tier by difficulty. The hardest changes (cross-cutting design, gnarly concurrency, subtle algorithms) go to your strongest judgment model (`claude-fable-5-thinking-max`) when the task needs judgment or the intent is vague, and to your strongest instruction-following model (`gpt-5.6-sol-max`) when the work is a precisely specified sequence of steps to execute to the letter; trivial mechanical edits go to your fast code model. Per-role lines in the `/setup-pstack` rule override these defaults and the model choices in the routed skills (`how`, `why`, `arena`, `swarm`, `architect`, `interrogate`, `reflect`); a role with no line keeps its default, and a role line of `inherit-parent` or `auto` runs that role on the parent chat model (omit Task `model`).
+**Defaults for every `Task` call.** `run_in_background: true`, agent mode (readonly strips MCP), file pointers not inlined context, explicit model per role (configurable via `/setup-pstack`; defaults `grok-4.6-fast-xhigh` for code, `claude-fable-5-1-thinking-max` for prose and judgment). Code delegates tier by difficulty. The hardest changes (cross-cutting design, gnarly concurrency, subtle algorithms) go to your strongest judgment model (`claude-fable-5-1-thinking-max`) when the task needs judgment or the intent is vague, and to your strongest instruction-following model (`claude-fable-5-1-thinking-max`) when the work is a precisely specified sequence of steps to execute to the letter; trivial mechanical edits go to your fast code model. Per-role lines in the `/setup-pstack` rule override these defaults and the model choices in the routed skills (`how`, `why`, `arena`, `swarm`, `architect`, `interrogate`, `reflect`); a role with no line keeps its default, and a role line of `inherit-parent` or `auto` runs that role on the parent chat model (omit Task `model`).
 
 You own every subagent's work. Review the diff and write your own summary, don't pass through what it said. Interrupt-chained resumes silently drop directives, so fire a fresh subagent with consolidated scope rather than trusting a "done" summary. A second opinion is the same prompt against a different model. Agreement is high-signal.
 

pstack/skills/poteto-mode/playbooks/bug-fix.md

@@ -6,7 +6,7 @@ Be scientific. Every shipped line traces to runtime evidence. Belt-and-suspender
 
 1. Reproduce it yourself on the matching surface via the control skill (Non-negotiables). Don't hand the repro to the user. A debug or instrumentation protocol that says to ask the user does not override this; you drive the instrumented runtime. Ask the user only with a stated, specific reason the control surface cannot reach the target, and only after driving it as far as it goes. Won't reproduce directly, force it: synthesize the trigger, tighten conditions, or instrument until it fires. A bug you can't reproduce, you can't prove fixed.
 2. Binary-search the cause. Form the candidate hypotheses, then rule them out until one survives. Seed them with `how` over the affected subsystem and the **why** skill for regression history. Each pass, take the split that cuts the most remaining problem space, get runtime evidence, eliminate. When program state is unclear, add instrumentation or logging and read it as the code runs. Don't guess. Drive a long or stubborn hunt with Cursor's `/loop` command. Confirm the surviving *mechanism* with runtime evidence before the step-3 architect/interrogate fan-out; a design grounded on a plausible-but-unconfirmed cause can be unanimously wrong while the real cause sits one subsystem over.
-3. Plan the fix. If it crosses a function boundary, `architect` first. Delegate implementation to a subagent using your configured bug-fix model (default `gpt-5.6-sol-max`) with a specific scope; review the diff.
+3. Plan the fix. If it crosses a function boundary, `architect` first. Delegate implementation to a subagent using your configured bug-fix model (default `claude-fable-5-1-thinking-max`) with a specific scope; review the diff.
 4. Verify on the same surface; the original repro now passes. "Inconclusive" or wrong-surface is not a pass; flag it. Unit tests show branch behavior, not bug absence.
 5. Stage the commits so the failing repro lands before the fix in git history; the diff tells the story. See the **tdd** skill for the failing-test-first cadence when the bug has a cheap local test path; skip it when the test would be expensive, integration-heavy, or unclear.
    This is the canonical **sequence-verifiable-units** principle skill, the failing test first and the fix on top.

pstack/skills/poteto-mode/playbooks/hillclimb.md

@@ -9,7 +9,7 @@ Core discipline: one change, one measurement, keep or revert. Never stack untest
 3. Open the decision log via the **show-me-your-work** skill. A `decision.tsv`, one row per attempt: id, hypothesis, change, before, after, delta, tests, verdict (kept or reverted), note. This is the run's memory. Read it before each attempt so the search accumulates instead of circling. Keep it out of the tree (gitignored) so it survives reverts.
 4. Ground each hypothesis in the architecture model from step 1, so it names a specific mechanism ("defer X off the boot path because it blocks first paint"), not "try memoizing something".
 5. Loop, one hypothesis per iteration:
-   - Hand the change to a subagent using your configured hillclimb model (default `gpt-5.6-sol-max`) with a tight scope; supervise and review the diff rather than typing it (the **guard-the-context-window** principle skill). When several independent hypotheses are live, fan them to parallel subagents, each in its own worktree so they can't collide (the **separate-before-serializing-shared-state** principle skill).
+   - Hand the change to a subagent using your configured hillclimb model (default `claude-fable-5-1-thinking-max`) with a tight scope; supervise and review the diff rather than typing it (the **guard-the-context-window** principle skill). When several independent hypotheses are live, fan them to parallel subagents, each in its own worktree so they can't collide (the **separate-before-serializing-shared-state** principle skill).
    - Measure before and after with the frozen harness, and run the regression gate.
    - Accept only when the metric moves past noise and the gate stays green. Otherwise revert the change in full; a tweak that "might help" does not ride along.
    - One commit per accepted fix, staging only the files you changed (`git add <files>`, never `-A`). Log the row either way, kept or reverted.

pstack/skills/poteto-mode/playbooks/perf-issue.md

@@ -13,7 +13,7 @@
    - **Redundancy.** The wait hangs on one slow instance or attempt. Duplicate the work (replicas, hedged requests, speculative execution) and take the fastest result. This trades extra load for lower tail latency, so the trace has to show the wait dominates and the system has headroom; duplication without that tradeoff only adds load.
    - **Lazy evaluation.** Cost lands on results that are never used or not needed yet (eager init on the boot path, rendering offscreen items). Defer the work until first use.
    - **Scheduling.** The work must happen, but not during the interactive moment. Move it to where nobody is waiting: idle callbacks, a background warmup after boot, precompute before the user arrives, cleanup after the frame commits. Distinct from Lazy (later-when-needed): Scheduling often runs the work *earlier* than the hot moment, or in its shadow. The win is perceived latency, so measure the interactive path, not total work done.
-3. Plan the fix from the trace. If it crosses a function boundary, `architect` first. Delegate implementation to a subagent using your configured perf-issue model (default `gpt-5.6-sol-max`); review the diff. Capture a post-fix trace.
+3. Plan the fix from the trace. If it crosses a function boundary, `architect` first. Delegate implementation to a subagent using your configured perf-issue model (default `claude-fable-5-1-thinking-max`); review the diff. Capture a post-fix trace.
    Apply the **sequence-verifiable-units** principle skill, verifying each attempt before trying the next.
 4. Parse and compare the artifacts (JSON to sqlite, diff). "Inconclusive" or wrong-surface is not a pass; flag it.
 5. Cite the measurement in the PR.