skills/roadmap-backcast/SKILL.md
Plans backward from a fixed goal or deadline to the present, identifying required milestones, dependencies, critical path, and feasibility constraints to transform aspirational targets into actionable sequenced plans. Use when planning with fixed deadlines, working backward from future goals, mapping critical path, or when user mentions "backcast", "work backward from", "reverse planning", "we need to launch by", "target date is", or "what needs to happen to reach".
npx skillsauth add lyndonkl/claude roadmap-backcastInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Copy this checklist and track your progress:
Roadmap Backcast Progress:
- [ ] Step 1: Define target outcome precisely
- [ ] Step 2: Work backward to identify milestones
- [ ] Step 3: Map dependencies and sequencing
- [ ] Step 4: Identify critical path
- [ ] Step 5: Assess feasibility and adjust
Step 1: Define target outcome precisely
State specific outcome (not vague goal), target date, success criteria. See Common Patterns for outcome definition examples. For straightforward backcasts → Use resources/template.md.
Step 2: Work backward to identify milestones
Start at end, ask "what must be true just before this?" iteratively. Create 5-10 major milestones. For complex multi-year roadmaps → Study resources/methodology.md.
Step 3: Map dependencies and sequencing
Identify what depends on what, what can run in parallel. See Dependency Mapping for techniques.
Step 4: Identify critical path
Find longest sequence of dependent tasks (this determines minimum timeline). See Critical Path Analysis.
Step 5: Assess feasibility and adjust
Compare required timeline to available time. Add buffers (20-30%), identify risks, adjust scope or date if needed. Self-check using resources/evaluators/rubric_roadmap_backcast.json before finalizing. Minimum standard: Average score ≥ 3.5.
Dependency types:
Sequential (A → B): B cannot start until A completes
Parallel (A ∥ B): A and B can happen simultaneously
Converging (A, B → C): C requires both A and B to complete
Diverging (A → B, C): A enables both B and C
Critical path: Longest sequence of dependent tasks (determines minimum project duration)
Finding critical path:
Example:
Milestone A (4 weeks) → Milestone B (6 weeks) → Milestone D (2 weeks) = 12 weeks (critical path)
Milestone A (4 weeks) → Milestone C (3 weeks) → Milestone D (2 weeks) = 9 weeks (non-critical, 3 weeks slack)
Critical path is 12 weeks (A→B→D path)
Managing critical path:
Pattern 1: Product Launch with Fixed Date
Pattern 2: Compliance Deadline (Regulatory)
Pattern 3: Strategic Transformation (Multi-Year)
Pattern 4: Event Planning (Conference, Launch Event)
Feasibility checks:
Common pitfalls:
Quality standards:
Resources:
resources/evaluators/rubric_roadmap_backcast.json5-Step Process: Define Target → Work Backward → Map Dependencies → Find Critical Path → Assess Feasibility
Dependency types: Sequential (A→B) | Parallel (A∥B) | Converging (A,B→C) | Diverging (A→B,C)
Critical path: Longest dependent sequence = minimum project duration
Buffer rule: Add 20-30% to estimates, 40%+ for high-uncertainty work
Feasibility test: Required time ≤ Available time (with buffer)
testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.