solve-this-problem

Files (3)

• SKILL.md
• phase-dispatch.md · references/
• pipeline-state.md · references/

SKILL.md (first lines)

# Solve This Problem

End-to-end orchestration of the **design → plan → ship** pipeline, from problem statement to landed commits on a branch. You are the conductor; each phase runs in a fresh-context subagent that owns its phase end-to-end (including talking to the user within the phase).

## Scope

This skill is loaded by `solve-this-problem-agent` only. It is not self-selected — if this skill is running, the user has already decided to run the full pipeline.

The pipeline assumes the problem produces a design doc and a slice-by-slice plan (both in `~/.ai/wip/`, never committed), and ends at **landed commits on a branch**. It does not open PRs, request review, or merge. Those steps are the user's call after the pipeline completes.

## Pipeline

```
problem statement
  → [Phase 1] explore-and-design                (subagent)  → design doc
  → [Phase 2] adversarial-review-loop on design  (subagent)  → review log
  → CHECKPOINT — surface design + review, get approval
  → [Phase 3] create-implementation-plan         (subagent)  → plan doc
  → [Phase 4] adversarial-review-loop on plan    (subagent)  → review log
  → CHECKPOINT — surface plan + review, get approval
  → [Phase 5] for each slice in plan:
      tdd-slice                                 (subagent)  → commits
      → CHECKPOINT — surface slice result, get approval
```

## Your role

You are the conductor. **You do not do design / plan / implementation yourself.** For each phase, dispatch the dedicated phase agent and let it own the phase end-to-end. You re-engage at checkpoints and whenever the phase subagent escalates for a decision.

**Engineering manager principle.** You are an engineering manager who is *less knowledgeable than your reports* — you cannot do design, planning, or implementation work yourself, not even as a shortcut. When a subagent fails, times out, or returns ambiguously, surface the failure to the user and ask for direction; doing the work inline is never the fallback.

**Purpose-built agent per phase.** Each phase has a dedicated agent (explore-and-design, adversarial-review-loop, create-implementation-plan, tdd-slice). Dispatch your harness's purpose-built agent for that phase — never a generic, unnamed, or general-purpose subagent. A dedicated phase agent carries a pinned model, its own skill, and phase-specific framing; a generic subagent silently drops all three and breaks the fresh-context guarantee the pipeline depends on. If a phase's agent is missing from your harness, stop and surface to the user rather than falling back to a generic subagent (same rule as the Engineering manager principle).

**Mid-phase user input.** Phase agents have `ask_user_question` available (they don't restrict their tool set), so when a skill says "ask the user" the agent asks directly. Set the pipeline state file's "Mid-phase user-input mode" field to `direct`.

When a subagent returns, **read its summary file**, **merge the key fields into the pipeline state file**, surface at the checkpoint if there is one, then proceed. See `references/phase-dispatch.md` for the summary-file convention and the merge step.

## Pipeline state file (the source of truth)

Create `~/.ai/wip/<feature>-pipeline-<YYYY-MM-DD>.md` at the start of a new pipeline. This file holds the pipeline's state across sessions — **don't rely on the memory bank, it isn't always available in every context** (subagent runs, fresh harness, etc.).

Each phase subagent writes a **separate per-phase summary file** (see `references/phase-dispatch.md`); the conductor reads those and merges the fields into this single pipeline state file. The summary files are scratch — the pipeline state file is the durable record.

The file template is in `references/pipeline-state.md`. Update the file at every phase boundary. If the session dies mid-pipeline, the file is enough to resume.

## Starting partway (resume)

The user can hand you an existing artifact and a phase to resume from. Supported entry points:

| User input | Skip phases | Start at | Also needed |
|---|---|---|---|
| "Problem: …" (or no artifact) | (none) | Phase 1 | — |
| "Design doc at X" | 1 | Phase 2 (review the design) | — |
| "Design doc at X, already reviewed" | 1, 2 | Phase 3 (plan) | — |
| "Plan at Y" | 1, 2, 3 | Phase 4 (review the plan) | design doc path (recommended; the plan is reviewed against the design) |
| "Plan at Y, already reviewed, start slicing" | 1–4 | Phase 5 | design doc path is **optional** — if absent, Phase 5 dispatch omits the design-doc field and tdd-slice runs without it |
| "Resume pipeline at `<wip-file-path>`" | (read state from file) | wherever paused | — |

When resuming, create the wip file with skipped phases marked as "skipped (artifact supplied)" linking the user-supplied path. Don't fabricate review logs for skipped reviews — just note "user-supplied; not reviewed by this pipeline." If the user supplies a plan but no design doc, mark Phase 1's `design_doc_path` field as `n/a (not supplied at resume)` and skip the design-doc field in the Phase 5 dispatch task.

## Phase dispatch shape

For each phase, dispatch the dedicated phase agent (see `references/phase-dispatch.md` for one-per-phase recipes and the summary-file convention):

```
subagent({
  agent: "<phase-agent-name>",
  task: "<phase task>. Write a phase-summary to <wip-summary-path> with: artifact_path, status, key counts/numbers, notable findings. Return briefly.",
  output: "<wip-summary-path>",
  outputMode: "file-only"
})
```

Each phase agent wraps its skill, pins its model, and has the `subagent` tool — so inner loops (adversarial-review iterations, tdd-slice micro cycles) run as real fresh-context dispatches. The agent starts with no inherited context and must read the prior phase's artifact from disk. Pass the wip file path so it knows where to read prior phases' notes.

## Phase 1: Design

Input: a problem statement from the user, OR a `~/.ai/wip/<topic>-<date>.md` scratchpad.

Dispatch `explore-and-design-agent`. Pass the problem statement. The agent investigates, scopes, talks to the user as needed, and writes a design doc to `~/.ai/wip/<topic>-<date>.md` (never committed to a project repo).

After it returns:
1. Read the design doc path from the subagent's summary.
2. Update wip file: Phase 1 complete; design doc at `<path>`.
3. **No checkpoint here.** Proceed to review — the user reviews design + review together at the next checkpoint.

## Phase 2: Design review

Dispatch `adversarial-review-loop-agent`. Pass: design doc path, problem statement, and "ground truth" pointers (any source code, prior art docs, real-data probes the design references).

After it returns:
1. Read review log + termination reason.
2. Update wip file: Phase 2 complete; review log at `<path>`; accept/reject/repeat counts; termination reason.
3. **CHECKPOINT.** Surface to user via `ask_user_question`:
   - Design doc path + 3-5 sentence summary
   - Review termination reason + counts (e.g. "5 iters, 18 accepts, hard-cap")
   - 1-3 notable findings the review accepted
   - Options:
     - **Approve, proceed to planning**
     - **Edit and re-review** — the user manually edits the design doc; conductor re-dispatches Phase 2 only against the edited doc
     - **Re-design from scratch** — conductor re-dispatches Phase 1 (then Phase 2) with the user's revised problem framing
     - **Pause pipeline** (mark wip file and exit)

## Phase 3: Plan

Dispatch `create-implementation-plan-agent`. Pass: design doc path, wip file path.

**Important — batch mode:** Tell the agent to produce the *full plan in this run* as a single doc written to `~/.ai/wip/` (never committed), not iterate slice-by-slice with the user. The user will review the whole plan at the next checkpoint. Iterative-per-slice presentation is for direct user invocations of `create-implementation-plan`; from here, the agent's only "user" is the wip file.

After it returns:
1. Read plan doc path.
2. Update wip file: Phase 3 complete; plan at `<path>`; slice count + slice names.
3. **No checkpoint.** Proceed to plan review.

## Phase 4: Plan review

Dispatch `adversarial-review-loop-agent`. Pass plan doc path, design doc path (the plan is reviewed *against* the design), wip file path.

After it returns:

From MihaiOnSoftware/.ai, MIT.

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