in8finity

Run N worker agents in parallel that drain the planning queue. Each worker loops: pull next task -> claim it (executing) -> do the work -> submit a report -> finish the task. Use when the user says "execute the plan", "run the queue", "spawn workers", or asks for parallel execution with a specific agent count.

Planning-board skill bundle for coordinating parallel coding agents with hashharness-backed append-only task, status, and report chains. Use this as the entry point to the bundled pm subskills under this directory, such as `plan`, `next`, `executing`, `report`, `finished`, `execute`, `replan`, `cancel`, `reclaim`, `sweep`, `dashboard`, `install`, `extract-steps`, and the `*-skill-execution` variants.

Cryptographic integrity attestation of a planning queue or the whole pm instance via hashharness `verify_work_package`. Re-checks every record's content hash, chain links, and schema binding across the queue's Task records plus each per-task TaskStatus / TaskReport / TaskHeartbeat chain — catching orphan records, tampered payloads, and `done` slipped in without proof. Unlike `verify_chain` (which followed a single named root), this covers every record in scope, closing the gap that pm's per-task chains have no common root. Use as a supervisor sweep before trusting a batch of agent output, or as a periodic integrity check.

Enqueue a new task on the planning board backed by hashharness. Creates an immutable Task record plus an initial TaskStatus(new). Supports subtasks (link to parent + parent's status at decision point) and dependency links to other tasks. Use when the user says "plan", "queue", "enqueue", "add task", or "create subtask".

Install the hashharness MCP backend that pm uses for its append-only storage. Bootstraps an isolated Python venv, installs hashharness from git, generates a launcher script and a source-able env file. Four install locations: ~/.hashharness/ (per-user, recommended), ~/.codex/hashharness/ (alongside Codex state), ~/.claude/hashharness/ (alongside Claude Code skills), or ./.hashharness/ (per-project at git root). Idempotent — re-running reports the existing install and exits 0. Use when a fresh workstation needs hashharness, when "pm setup" fails because no MCP server is reachable, or when you want a sandboxed second instance for testing.

Extract the major workflow steps from another skill's SKILL.md, with bash-side validation that every extracted step's anchor is verbatim in the source (catches LLM hallucination), and optional recursion into nested skill invocations (step X of skill A invoking skill B yields nested steps named `X.B.S1`, `X.B.S2`, ...). Two modes — llm (semantic, default when a supported LLM CLI is on PATH) and regex (pattern- matching fallback). Used internally by pm-auto-skill-execution, pm-assisted-skill-execution, pm-guided-skill-execution; also callable standalone for inspecting a skill's structure.

Mark a task as done (or rejected) on the planning board. Appends a terminal TaskStatus that links proof -> latest TaskReport on the task. Refuses if no TaskReport exists, enforcing "task to be finished requires proof of work/report". If the Task declares a verifier at plan time, this script runs it before allowing `done`. Use when an agent has submitted its final report and wants to close the task; pass --rejected to close as failed instead.

Drive another skill step-by-step through the planning queue, with the user in the loop at every gate. Extracts the target skill's steps from its SKILL.md, plans one task per step (chained by dependsOn), and executes them one at a time — pausing after each step to surface decisions, accept user-supplied subtask requests, and confirm before moving on. Use when the user says "guided run", "step through skill X", "drive skill X with user-in-the-loop", or asks for an execution that respects a skill's prescribed dialogue gates.

Structured bug investigation using formal models for hypothesis-driven debugging, with hashharness MCP storage as the append-only backend (experimental variant of formal-debugger). Builds scoped Alloy models (normative rules, data constraints, causal chains, observability) to generate distinguishing experiments and narrow the causality cone around a symptom. Use whenever the user reports a bug, incident, or unexpected behavior and wants rigorous investigation backed by hashharness's append-only hash-chained storage. Trigger on "investigate", "root cause", "debug this", "why does this happen", "find the bug", "postmortem", or any unexplained symptom. Valuable for: business-rule bugs, state machine errors, workflow inconsistencies, cross-service disagreements, investigations where the obvious explanation was already checked. Not for simple stack traces or typos — use when the problem space is large enough that unstructured search would waste time or miss the real cause.

EXPERIMENTAL — UNDER TESTING. Unstable variant; behaviour and gates may change without backwards-compatible migration. Structured bug investigation using textual formal models for hypothesis-driven debugging, with hashharness MCP storage as the append-only backend (textual-only variant of formal-debugger, no solver execution). Builds scoped textual models (state machines, sequence diagrams, schemas, invariant tables — see `references/representation-menu.md`) covering normative rules, data constraints, causal chains, and observability, then walks them by hand against collected evidence. Use whenever the user reports a bug, incident, or unexpected behavior and wants rigorous investigation backed by hashharness's append-only hash-chained storage AND wants to avoid Alloy/Dafny tooling. Trigger on "investigate", "root cause", "debug this", "why does this happen", "find the bug", "postmortem", or any unexplained symptom. Valuable for: business-rule bugs, state machine errors, workflow inconsistencies, cross-service

Structured bug investigation using formal models for hypothesis-driven debugging. Builds scoped Alloy models (normative rules, data constraints, causal chains, observability) to generate distinguishing experiments and narrow the causality cone around a symptom. Use whenever the user reports a bug, incident, or unexpected behavior and wants rigorous investigation. Trigger on "investigate", "root cause", "debug this", "why does this happen", "find the bug", "postmortem", or any unexplained symptom. Valuable for: business-rule bugs, state machine errors, workflow inconsistencies, cross-service disagreements, investigations where the obvious explanation was already checked. Not for simple stack traces or typos — use when the problem space is large enough that unstructured search would waste time or miss the real cause.

Write, run, and interpret Alloy 6 formal models for software systems, business processes, and skill/workflow design. Use this skill whenever the user asks to model, verify, or prove properties about state machines, lifecycle flows, data invariants, API contracts, integration boundaries, permission systems, or business rules. Also trigger when the user mentions Alloy, formal methods, formal verification, model checking, counterexample, temporal logic, or wants to "prove" something about their system. Use even for seemingly simple state-machine questions — the solver catches edge cases that humans miss. This skill applies to any domain: SaaS, e-commerce, fintech, DevOps, or business process design.

Structured bug investigation using formal models for hypothesis-driven debugging. Builds scoped Alloy models (normative rules, data constraints, causal chains, observability) to generate distinguishing experiments and narrow the causality cone around a symptom. Use whenever the user reports a bug, incident, or unexpected behavior and wants rigorous investigation. Trigger on "investigate", "root cause", "debug this", "why does this happen", "find the bug", "postmortem", or any unexplained symptom. Valuable for: business-rule bugs, state machine errors, workflow inconsistencies, cross-service disagreements, investigations where the obvious explanation was already checked. Not for simple stack traces or typos — use when the problem space is large enough that unstructured search would waste time or miss the real cause.

Write, run, and interpret Alloy 6 formal models for software systems, business processes, and skill/workflow design. Use this skill whenever the user asks to model, verify, or prove properties about state machines, lifecycle flows, data invariants, API contracts, integration boundaries, permission systems, or business rules. Also trigger when the user mentions Alloy, formal methods, formal verification, model checking, counterexample, temporal logic, or wants to "prove" something about their system. Use even for seemingly simple state-machine questions — the solver catches edge cases that humans miss. This skill applies to any domain: SaaS, e-commerce, fintech, DevOps, or business process design.