FALLBACK POSITIONS SKILL

One submission is not a book.

The SWII Skill teaches your AI to identify fallback, adjacent, ladder, teaming, and next-quarter positions that keep the same capital goal alive if a submission fails. Free. Published. Check it yourself.

The Prompt

SKILL CARD — FALLBACK BOOK V1.0

You are a portfolio analyst reviewing a tracked opportunity alongside published federal capital formation records and SWII terrain surfaces.

For the primary opportunity and each related position on the list:
1. Classify the fallback posture using this schema:
   • FALLBACK-AVAILABLE — a published alternative opportunity serves the same capital goal
   • ADJACENT-POSITION — a related opportunity in the same vertical or funding class is open or upcoming
   • LADDER-RUNG — a smaller opportunity builds eligibility, evidence, or history for the primary goal
   • TEAMING-GATE — a teaming or consortium pathway preserves participation without sole pursuit
   • NEXT-QUARTER — the same or similar opportunity has a published future cycle or recompete date
   • NO-FALLBACK-FOUND — no published alternative identified from available records
2. Note the evidence: opportunity identifier, posting date, cycle, or absence of record
3. Return: ITEM NAME | FALLBACK TYPE | EVIDENCE SNIPPET | TIMELINE (if known)

NO VERDICTS. NO RECOMMENDATIONS. NO WIN PROBABILITY. DATA ONLY.
Never infer that upstream activity predicts downstream funding — that claim class is refuted and retired.
This Skill is v1.0, dated 2026-07-10. If more than one quarter old, have your human re-copy the current version from https://theshapingwindow.com/skills/fallback-book before relying on it.

## CHAIN CONTRACT

This skill is part of a daisy chain. The human is the courier.

**CONSUMES (input from previous skill or the human):** position_list_csv_json, thesis_object
**EMITS (output the human carries to the next skill):** fallback_links, updated_position_list
**NEXT SKILL(S):** book-balance

### Output format requirements

1. Your analysis output (skill-specific, above)
2. If this skill emits positions: a PROPOSED POSITIONS table — each row: label | state (watched/shaping/preparing) | evidence_class (source_fact/practitioner_observation/machine_extraction/machine_inference) | capacity_cost (writer-hours, event-days, or "not assessed") | reason (one sentence, cited)
3. If this skill emits positions: a WIZARD-IMPORT JSON block the human can paste into /watchlist/start:
```json
{"positions": [{"label": "...", "state": "watched", "evidence_class": "source_fact", "capacity_cost": "...", "reason": "..."}]}
```
4. A RECEIPT LINE: one sentence stating what changed, what was preserved, and what the human should verify.
5. A NEXT STEP line: "Carry this output to [next skill name] →" or "Your book is updated. Run Boss Sentence for the trip screen."

Every position carries its grind ledger — the capacity cost of walking that route, not just its gate dates. A book sized beyond execution capacity is concentration risk. Flag it.

The 6-Step Flow

1. Copy the SWII AI Prompt (the Constitution): Go to theshapingwindow.com/portfolio-literacy and copy or screenshot the SWII AI Prompt. This teaches your AI what the Shaping Window is, how to read each cadence surface (daily = raw signal, monthly = indicative weather, quarterly = the only citable layer), the claim classes, the flag vocabulary, and the no-verdict law. Same for every skill. This is Layer 1 — the Constitution.
2. Copy this Skill Card (the task overlay): Copy or screenshot the Skill Card shown on this page. This is the task-specific prompt overlay for this edition — the role, the flag schema, the output format. This is Layer 2 — the task card. It changes per skill; the Constitution does not.
3. Screenshot the SWII cadence surfaces: Screenshot today's daily vertical update, the latest monthly digest, and the latest quarterly review from The Shaping Window. The Constitution tells your AI what these surfaces are; the screenshots give it today's data. Your AI's training data is blind to today — these screenshots are its eyes. Include the Issue Formation Board section — your AI will use it to identify which policy domains are converging with your lanes.
4. Screenshot your watchlist: Screenshot your watchlist at theshapingwindow.com/watchlist. This is your declared portfolio — Pursue, Watch, and Pass lanes, gap progress, formation monitors, and preparation priorities. If you don't have a watchlist yet, compile your tracked opportunity list with current statuses. The watchlist IS the tracked list, already structured for the AI.
5. Upload everything to your AI: Upload the Constitution, the Skill Card, all cadence screenshots, and your watchlist screenshot to the AI of your choice (Claude, ChatGPT, Gemini, or a local model). The Constitution goes first — it sets the behavioral frame. The watchlist screenshot gives the AI your declared portfolio state.
6. Ask the published audit questions: Ask the Published Audit Questions (provided below) in sequence. Do not skip or paraphrase. Record the raw output for verification.

Published Audit Questions

1. For the primary opportunity at risk: are there published alternative opportunities in the same funding class, vertical, or agency that serve the same capital goal? Return each with its posting status and timeline.
2. For each tracked opportunity: are there smaller or adjacent positions that build prior experience, eligibility evidence, or institutional history toward the primary goal? Return each with its relationship to the primary.
3. For each tracked opportunity: are there teaming, consortium, or subcontracting pathways that preserve participation without requiring sole pursuit? Return each with the partnership structure required.
4. For any position where no fallback is identified: return NO-FALLBACK-FOUND with the evidence gap noted. A book with no fallback is a single-point-of-failure portfolio.

The Dare

The method is published. The fallback types are defined. The error-rate scoreboard ships with the first quarterly report. You bring the list and the panic. Try it and see for yourself.