pstack: parity sweep with the private skill tree

pstack/.cursor-plugin/plugin.json

@@ -1,7 +1,7 @@
 {
 	"name": "pstack",
 	"displayName": "pstack",
-	"version": "0.11.2",
+	"version": "0.11.3",
 	"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/skills/poteto-mode/SKILL.md

@@ -18,9 +18,9 @@ Remaining triggers:
 
 - Nontrivial change, architecture decision, or "are we sure?" → the **how** skill.
 - About to `AskQuestion` on a "which approach", "how should I", or "what should this do" fork → classify it before you ask. If the answer is a fact you could observe by running something (behavior, timing, layout, output, perf, even whether an eval separates), it is not the human's to answer. Sketch it via the Prototype playbook (`playbooks/prototype.md`) and let the result decide. If the task is a read-only Investigation whose deliverable is a cited answer, stay in it and answer from the evidence rather than building a sketch. Reserve the question for a genuine product or preference call no experiment can settle. The ask is the slow path. A throwaway probe usually answers faster, and it hands the human a result to react to instead of a decision to make.
-- Any code → name the data shape first.
+- Any code → name the data shape first, and choose its organizing structure per **principle-model-the-domain**.
 - Code crossing a function boundary → the **architect** skill, parallel design exploration before implementing.
-- Contested design → the **interrogate** skill (four-model adversarial) before shipping.
+- Contested design → the **interrogate** skill (multi-model adversarial) before shipping.
 - Nontrivial multi-step → write the throughput checkpoint (Feature step 3).
 - Any prose surface → the **unslop** skill. Your reply is a prose surface; write it per **Writing the reply**. Agent-facing prose also follows the **create-skill** skill (Cursor's built-in for authoring SKILL.md files).
 - Before commit → the `deslop` skill from the `cursor-team-kit` plugin (`/deslop`).
@@ -84,7 +84,7 @@ 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`) 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-4.5-fast-xhigh` for code, `claude-opus-4-8-thinking-xhigh` for prose and judgment). The hardest changes (cross-cutting design, gnarly concurrency, subtle algorithms, vague intent) go to `claude-fable-5-thinking-max`.
+**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-4.5-fast-xhigh` for code, `claude-opus-4-8-thinking-xhigh` 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 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`, `architect`, `interrogate`, `reflect`); a role with no line keeps its default.
 
 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/authoring-a-skill.md

@@ -7,6 +7,6 @@
 3. Test cases if structural; skip if subjective.
 4. Run **Opening a PR**.
 
-When in doubt, delete; prose earns its keep by changing a decision. Match tone to scope. Point at structural sources (types, READMEs, config); hardcoded details go stale (the **encode-lessons-in-structure** principle skill). Delegate to other skills by path; don't restate. A workflow you keep hitting but isn't captured → propose a new skill.
+When in doubt, delete; prose earns its keep by changing a decision. Tell it to do the thing and skip the reason. Explain only when the rule is confusing without one. Match tone to scope. Point at structural sources (types, READMEs, config); hardcoded details go stale (the **encode-lessons-in-structure** principle skill). Delegate to other skills by path; don't restate. A workflow you keep hitting but isn't captured → propose a new skill.
 
 **Reply:** summary of the skill, key design decisions, validation notes.

pstack/skills/poteto-mode/playbooks/autonomous-run.md

@@ -2,11 +2,11 @@
 
 **You own the exit condition. Define done, then drive to it without stopping.** For "going to bed" / "run until done" / "/loop until X".
 
-1. State the exit condition as a checkable predicate before the first iteration (tests green, repro fixed, all N PRs merged, pixel-diff zero).
+1. State the exit condition as a checkable predicate before the first iteration (tests green, repro fixed, all N PRs merged, pixel-diff zero). A vague goal stalls; a predicate lets you stop.
 2. Pick the wake mechanism using Cursor's `/loop` command (a built-in, not a pstack skill). An event to watch (CI, a merge, a ref advancing) gets a watcher subagent that wakes you on the event, with a long time-based heartbeat as fallback. No event gets a fixed-interval heartbeat sized to when the result is worth re-checking.
 3. Each iteration makes the smallest change the evidence justifies, verifies it against the predicate, commits if it advanced, discards changes that didn't help. Belt-and-suspenders that "might help" gets reverted, not left to ride.
    Sequence the work via the **sequence-verifiable-units** principle skill, verifying each unit before the next instead of batching checks at the end.
-4. Checkpoint every iteration via the **show-me-your-work** skill, a row for what changed and whether the predicate moved.
+4. Checkpoint every iteration via the **show-me-your-work** skill, a row for what changed and whether the predicate moved. A run with no trail can't be audited or resumed.
 5. Stop when the predicate is met. A plateau is not a stop, so keep going and pivot your approach to push past it. Surface a genuine dead end rather than spinning, and never relax the predicate to declare victory.
 
 **Reply:** the exit condition, iterations run, what landed, what was discarded, final predicate state.

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

@@ -4,8 +4,8 @@
 
 Be scientific. Every shipped line traces to runtime evidence. Belt-and-suspenders that "might help" is a hypothesis, not a fix; it does not ship. When evidence refutes a hypothesis, revert what it motivated. The smallest change the evidence justifies ships, nothing more. Same discipline for Perf, where the evidence is the trace.
 
-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.
-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.
+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-5.5-high-fast`) 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.

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

@@ -9,7 +9,7 @@
    - **Independent workstreams.** Disjoint files, services, or layers parallelize. Shared writes serialize.
    - **Shared mutable state.** Default to splitting the target (the **separate-before-serializing-shared-state** principle skill). Serialize only for real invariants.
    - **Smallest safe decomposition.** If one worker is best, name why.
-4. Delegate code-writing to a subagent using your configured feature model (default `grok-4.5-fast-xhigh`) with a specific scope (file paths, named data shape, success criteria); review its diff yourself. When the implementation admits multiple valid shapes (error handling, abstraction layer, test structure), delegate via the **arena** skill instead so the runners surface the alternatives and the cross-judge guards the pick. Mandatory: no skip-with-reason escape, and Laziness Protocol does not override it (the gain is review separation, not lines saved). You can spawn a subagent even though you are one; "the app is small" and "a subagent cannot spawn one" are both wrong. A subagent forbidden to spawn satisfies this by owning the diff directly with the same review separation; no "standing by" reply that waits on a nested agent. Comments per **Comments**. Surgical edits, re-ground against the source for upstream-derived files. Port shared-primitive improvements to all consumers and verify each. Commit liberally.
+4. Delegate code-writing to a subagent using your configured feature model (default `grok-4.5-fast-xhigh`) with a specific scope (file paths, named data shape and its organizing structure per **principle-model-the-domain** — a state machine over scattered booleans, a table/registry over branching, a typed model over repeated shape assumptions, chosen before the delegate writes logic — and success criteria); review its diff yourself. When the implementation admits multiple valid shapes (error handling, abstraction layer, test structure), delegate via the **arena** skill instead so the runners surface the alternatives and the cross-judge guards the pick. Mandatory: no skip-with-reason escape, and Laziness Protocol does not override it (the gain is review separation, not lines saved). You can spawn a subagent even though you are one; "the app is small" and "a subagent cannot spawn one" are both wrong. A subagent forbidden to spawn satisfies this by owning the diff directly with the same review separation; no "standing by" reply that waits on a nested agent. Comments per **Comments**. Surgical edits, re-ground against the source for upstream-derived files. Port shared-primitive improvements to all consumers and verify each. Commit liberally.
 5. Verify on the matching surface. "Inconclusive" or wrong-surface is not a pass; flag it.
 6. Rebase into small, ordered commits; stack follow-ups.
    Use the **sequence-verifiable-units** principle skill, building, verifying, and committing each small unit before the next.

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

@@ -4,10 +4,10 @@
 
 Core discipline: one change, one measurement, keep or revert. Never stack untested changes, and never claim a win from code inspection. The data decides (the **prove-it-works** principle skill).
 
-1. Fix the metric and the stop predicate before the first attempt. One number, the direction that counts as better, and a checkable predicate that pairs a target with a floor on attempts so a lucky early win can't end the run (the example "at least 50% better than baseline and at least 10 iterations" is this shape). Use the user's numbers when given, otherwise agree them. A vague goal spins; a predicate lets you stop.
-2. Build the measurement harness, then freeze it (the **build-the-lever** principle skill). One repeatable command that emits the metric, sampled enough to clear the noise (median of N, not a single run). It is the ruler, so once it produces the baseline it is immutable; changing it mid-run invalidates every earlier number. Record the baseline metric and a green run of the regression gate (the tests that must keep passing) before any change.
+1. Ground the workload and architecture before choosing the ruler. Run the **how** skill over the target, name the realistic workload dimensions that can move the result (data size, history, state, concurrency), and select a case that reproduces the user's complaint. If no case reproduces it, fix the repro instead of hillclimbing. Then fix one metric, the direction that counts as better, and a checkable stop predicate that pairs a target with a floor on attempts so a lucky early win can't end the run (the example "at least 50% better than baseline and at least 10 iterations" is this shape). Use the user's numbers when given, otherwise agree them.
+2. Build the measurement harness, prove its sensitivity, then freeze it (the **build-the-lever** principle skill). Run contrasting realistic workloads and confirm the target case reproduces the symptom while easier cases separate as expected. If the ruler cannot distinguish them, revise the workload or metric. Once frozen, one repeatable command emits the metric, sampled enough to clear the noise (median of N, not a single run); changing it invalidates every earlier number. Record the baseline metric and a green run of the regression gate (the tests that must keep passing) before any change.
 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 hypotheses in the real architecture before guessing. Run the **how** skill over the target once, up front, so each attempt names a specific mechanism ("defer X off the boot path because it blocks first paint"), not "try memoizing something".
+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-5.5-high-fast`) 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.