/haiku-pilot

name: haiku-pilot
description: |
  Haiku-first execution playbook: through deliberate prompt structure, sub-agent
  delegation, and quantitative escalation gates, get Haiku 4.5 to produce
  near-Opus quality on most tasks. Escalate to Sonnet/Opus only when gates
  trigger.
  Triggers: "haiku", "Haiku", "Haiku mode", "haiku-pilot".

  Do NOT use for: cost-only token optimization, file-count cognitive heuristic,
  agent dispatch table, CLAUDE.md / rules audit, harness health check.
  This SKILL is a runtime router + escalation gate, not a decision tree
  or directory.
allowed-tools: Read, Grep, Glob, Bash, TodoWrite

Haiku Pilot — Default-Haiku Execution Playbook

Thesis

**Weak planner + strong delegation > strong planner doing everything.**

> AgentOpt (arxiv 2604.06296): On HotpotQA, Opus alone = 31.71%; Ministral 3 8B planner + Opus solver = 74.27%. > Augment Code eval (2026-04): Best-quality AGENTS.md provides performance gain equivalent to one model tier (Haiku → Sonnet effective).

→ Default parent session to **Haiku 4.5**; aggressively delegate to sub-agents; **escalate only on quantitative gate trigger**.

> "Documentation / process design improvements ≥ model upgrade" is the engineering basis. Blindly upgrading models in multi-stage pipelines often *reduces* performance (planner doesn't delegate).

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Per-Session Pre-flight (run once per task)

1. Reference Pattern Technique

| ❌ Verbal description | ✅ Point to a file | |---------------------|-------------------| | "Write a login-like function" | "Follow the pattern in `src/auth/login.ts`" | | "Add a hook similar to useUser" | "Pattern: `hooks/useUser.ts:42-78`" |

**Why it matters**: Haiku diverges easily from abstract descriptions; pointing to a concrete file = strongest possible context.

2. Think-Before-Coding (compensates Haiku tendency)

Before implementation, output:

• 1–2 sentence **task understanding restatement** (your interpretation, not parroting)
• Key assumptions (≥ 1, prefixed with "Assumption: ...")
• If multiple interpretations are reasonable → **list options for user confirmation**, don't pick one yourself

> Source: `core.md` § "Think-Before-Coding". Haiku tends to skip this step; this SKILL enforces it.

3. Diff-Review Pledge

Before declaring "complete", run:

git diff --stat
git status

**Explain each change in one sentence**. Catches Haiku quietly modifying out-of-scope files (surgical-changes discipline).

4. Citation Anchor Enforcement

When citing wiki / docs / spec for numbers, conclusions, or anti-patterns, attach a structured anchor:

• `(P0X §Y.Z)` — paper section ref (e.g. `(P03 §4.2)`) — **preferred** for paper-grounded citations
• `(<source> Step N)` — wiki step number
• `(anti-pattern #N)` — wiki anti-pattern number
• `(<file>:line-range)` — code / config line ref

**Anchor count requirement** (per answer paragraph):

• Easy / medium tasks: ≥ 3 structured anchors
• **Hard tasks** (architecture / counter-factual / synthesis): **≥ 5 structured anchors** — `(P0X §Y.Z)` format strongly preferred

If no precise anchor available → tag `[unverified]`. Never fake a source. Violation: retry that paragraph; do NOT declare done.

> Empirical basis: anchor density discrepancy is the primary driver of citation accuracy variance between Haiku and Sonnet/Opus on benchmark tasks. 2026-05-08 6-agent 10Q benchmark: Haiku-Pilot hard-question anchor density collapsed from 7/100w (medium) to 1/100w (hard), the primary D2 gap (−1.0) vs vanilla Opus. Pre-flight previously did not require ≥ 5 anchors on hard tasks; this rule closes that gap. > Per-source-type anchor vocabulary (wiki / code / API doc / RFC / paper): see shared reference `anchor-dictionary.md` (suite-internal) if available.

5. Source-Verify Loop (required for citation tasks)

After drafting an answer that cites numbers, model names, or verbatim quotes, run this 3-step loop **before declaring done**:

1. **Identify**: list every numeric / proper-noun citation in your answer (e.g. "31.71%", "Claude Opus 4.6", "≥ 100 lines"). 2. **Grep**: for each citation, run `grep -i "<number-or-name>" <source-path>`. If absent → the citation is fabricated. 3. **Record**: in the self-check table § "Source-verify" row, mark ✓ only when **every** citation passes step 2.

| Failure mode caught | Example from v0.2.1 benchmark | |---------------------|-------------------------------| | Cross-paper number mis-attribution | Q06: "5.6× cognitive load" attributed to P06/P07 (actually a benchmark-internal number) | | Fabricated benchmark scores | Q12: "Opus+HumanLayer=55%, Haiku=40%" — neither in source | | Non-existent model version | Q13: "Claude 3.5 Opus" — no such Anthropic-published version | | Invented latency / token claims | Q17: "p99 < 100ms / 500K tokens" — not in P08 | | Vague paper-grounded targets | Q18: "failure rate ↓ 60–80%" — paper has no such range |

If any cited number fails grep, **rewrite that paragraph** before completion. **Do not** ship with `[unverified]` tags as a workaround on citation tasks; the gate is binary.

> Empirical basis: 2026-05-06 v0.2.1 benchmark — 5 of 20 haiku-pilot responses (25%) shipped fabrications, capping Haiku→Opus gap closure at 45.3%. Adding this gate is the highest-ROI lever per gap analysis.

6. Content-First Structure (not Template-First)

**Forbidden**: filling every question with a fixed sub-heading template; restating in prose what the table already says.

**Use instead**: the question's nature determines the structure:

• Single decision → bulleted list
• ≥ 3 anti-patterns → 1 combined table (with "trigger / solution / Lab vs Prod" columns), NOT N separate tables
• Multi-option comparison → table lists only "final decision + exclusion rationale", does NOT enumerate all rejected options
• Emoji budget: ≤ 3 per section (✅/❌/🔴); neutral statements get none

> Empirical basis: template-first wastes ~30% tokens AND lowers detail density vs content-first.

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Per-Task Route