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Get Started Free →Headless implementation-plan authoring for the Foreman planning stage. Explore the target repo first, then write a deep, decomposition-aware plan that the grill→ADR/PRD→issues pipeline can build on — goals, seams, data/interface changes, risks, sequencing, and testing strategy. No placeholders. Writes the plan body and stops.
.claude/skills/visionforge-ou-foreman-plan/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 96% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 532% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 522% | 0% |
| case-20 | ✓→✗ | ▼ Worse | 217% | 0% |
| case-07 | ✓→✗ | ▼ Worse | -83% | 0% |
(Adapted from obra/superpowers writing-plans (MIT) — see NOTICE. Re-aimed at Foreman's planning stage: this plan is the input to the grill stage, which turns it into an ADR + PRD, which foreman-to-issues then slices — so it stays at the design/seam level and does NOT emit the per-step TDD checklist that foreman-to-issues and foreman-tdd own. Removed the interactive execution-handoff prompts; there is no live human in this run.)
You are the planner, running headless. Write a deep implementation plan for the feature request, grounded in this repository. Produce the plan body as markdown at the exact path Foreman gives you (body only — no YAML frontmatter) and stop. Do not ask questions; anything you genuinely cannot resolve is recorded as an explicit assumption or open risk for the grill stage to challenge.
Assume the reader is a skilled engineer who knows almost nothing about this codebase. Before proposing anything, learn the ground truth:
CONTEXT.md / CONTEXT-MAP.md if present and use theproject's canonical terms throughout.
docs/adr/. Respect accepted ADRs; if your plan mustcontradict one, say so explicitly — that is a decision the grill stage will weigh.
Verify your assumptions against what the code actually does.
Before writing tasks, map which files/modules will be created or changed and the one responsibility of each. Design units with clear boundaries and well-defined interfaces; prefer small, focused files over large ones that do too much; files that change together live together. In an existing codebase, follow established patterns rather than restructuring unilaterally.
Cover, scaled to the feature's complexity:
key trade-offs. Name the alternatives you rejected and why (this is what the grill stage will pressure-test).
migration/backfill and backward compatibility.
observability.
that each is independently verifiable (the raw material foreman-to-issues will cut into issues). Keep this to the shape of the work, not a line-by-line script.
Every section must carry real content. These are plan failures — never write them:
Read the request again with fresh eyes against your plan:
match what you introduced earlier?
Fix issues inline, then write the file and stop. On a revision pass (Foreman gives you your prior plan and reviewer comments) keep everything that still applies, address every comment, and end with a ## Changelog noting what changed and which comment drove it.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-20 | pass→fail | 13,269 | 40,162 | +203% | 1 | 1 | 0% | 2,235 | 7,080 | +217% | 0 | 0 | — |
case-01 | fail→fail | 4,918 | 3,683 | -25% | 1 | 1 | 0% | 226 | 1,135 | +402% | 0 | 0 | — |
case-02 | fail→fail | 3,731 | 4,667 | +25% | 1 | 1 | 0% | 248 | 1,101 | +344% | 0 | 0 | — |
case-03 | fail→fail | 3,156 | 37,186 | +1078% | 1 | 1 | 0% | 247 | 1,178 | +377% | 0 | 0 | — |
case-09 | fail→fail | 16,257 | 2,517 | -85% | 1 | 1 | 0% | 2,681 | 996 | -63% | 0 | 0 | — |
case-04 | fail→pass | 20,799 | 37,372 | +80% | 1 | 1 | 0% | 3,614 | 7,100 | +96% | 0 | 0 | — |
case-05 | fail→pass | 6,446 | 30,778 | +377% | 1 | 1 | 0% | 988 | 6,247 | +532% | 0 | 0 | — |
case-06 | fail→pass | 7,800 | 33,578 | +330% | 1 | 1 | 0% | 1,140 | 7,092 | +522% | 0 | 0 | — |
case-07 | pass→fail | 38,898 | 3,641 | -91% | 1 | 1 | 0% | 6,190 | 1,058 | -83% | 0 | 0 | — |
case-08 | pass→fail | 16,149 | 38,330 | +137% | 1 | 1 | 0% | 2,734 | 7,075 | +159% | 0 | 0 | — |
case-10 | fail→fail | 20,259 | 5,967 | -71% | 1 | 1 | 0% | 3,832 | 1,222 | -68% | 0 | 0 | — |
case-11 | fail→fail | 10,678 | 4,723 | -56% | 1 | 1 | 0% | 1,809 | 1,142 | -37% | 0 | 0 | — |
case-12 | fail→fail | 27,066 | 5,683 | -79% | 1 | 1 | 0% | 4,446 | 1,209 | -73% | 0 | 0 | — |
case-13 | fail→fail | 9,294 | 7,071 | -24% | 1 | 1 | 0% | 1,569 | 1,390 | -11% | 0 | 0 | — |
case-14 | pass→fail | 34,918 | 3,347 | -90% | 1 | 1 | 0% | 6,066 | 1,040 | -83% | 0 | 0 | — |
case-15 | pass→fail | 20,560 | 4,765 | -77% | 1 | 1 | 0% | 3,436 | 1,099 | -68% | 0 | 0 | — |
case-16 | fail→fail | 29,044 | 4,363 | -85% | 1 | 1 | 0% | 4,686 | 1,016 | -78% | 0 | 0 | — |
case-17 | pass→fail | 21,180 | 4,871 | -77% | 1 | 1 | 0% | 3,896 | 1,195 | -69% | 0 | 0 | — |
case-18 | fail→fail | 26,441 | 6,807 | -74% | 1 | 1 | 0% | 3,299 | 1,122 | -66% | 0 | 0 | — |
case-19 | fail→fail | 18,794 | 4,998 | -73% | 1 | 1 | 0% | 3,005 | 1,090 | -64% | 0 | 0 | — |
case-21 | pass→fail | 9,573 | 31,323 | +227% | 1 | 1 | 0% | 1,792 | 6,091 | +240% | 0 | 0 | — |
case-22 | pass→fail | 10,240 | 4,707 | -54% | 1 | 1 | 0% | 1,558 | 1,203 | -23% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 6 counted toward the lift figure. The other 16 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of -23 percentage points is the difference between those two pass rates over the 6 comparable cases. 8 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.