Agent skill
lp-do-idea-forecast
Build a 90-day startup forecast and proposed goals from a business idea and product specs using web research (competitor analysis, price bands, channel benchmarks, demand signals). Use when a user provides idea/product inputs and wants the agent to set most targets, assumptions, risks, and validation metrics before SFS-00 or lp-do-idea-generate.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/idea-forecast
SKILL.md
Ideas Forecasting
Use this skill to invert planning: user provides the business idea and products, then the agent proposes practical 90-day targets from external evidence.
Invocation
/lp-do-idea-forecast
/lp-do-idea-forecast --biz=HEAD
/lp-do-idea-forecast --biz=PET --country=IT --horizon-days=90
/lp-do-idea-forecast --biz=PET --launch-surface=pre-website
/lp-do-idea-forecast --biz=PET --market-research-max-age-days=30
Defaults:
horizon-days=90country=ITif not providedlaunch-surface=website-liveunless user states no site is readymarket-research-max-age-days=30- focus: speed to first sales for startup businesses
Inputs
Minimum required:
Business code: e.g.HEAD,PETBusiness idea: one-paragraph conceptProducts: list of products with short specs
Strongly recommended:
Price intent(if known): target price or rangeChannels: DTC site, marketplaces, social commerce, retail, etc.Budget guardrails: launch budget cap and ad budget guardrailStock timeline: date first sellable stock is availableLaunch surface:website-liveorpre-websiteConstraints: legal, ops, brand, geography, supply chain
Market Intelligence Pack (Prerequisite)
Canonical files:
docs/business-os/market-research/<BIZ>/latest.user.mddocs/business-os/market-research/<BIZ>/<YYYY-MM-DD>-market-intelligence.user.md
Readiness rule:
- If
latest.user.mdis missing,Statusis notActive, orLast-reviewedis older thanmarket-research-max-age-days, treat market research as stale.
When stale:
- Stop forecasting.
- Present a detailed Deep Research prompt to the user using:
docs/business-os/market-research/_templates/deep-research-market-intelligence-prompt.md
- Fill placeholders from current intake packet and return a copy-paste prompt block.
- Resume forecast only after research output is supplied.
Launch Surface Modes
website-live
Use standard ecommerce funnel forecasting.
Core forecast metrics:
- sessions
- conversion rate
- AOV
- orders
- gross revenue
- gross margin
- CAC
pre-website
Use this when no production website is ready. Forecast around early sales validation channels and assisted commerce.
Core forecast metrics:
- qualified leads
- assisted order attempts
- assisted order conversion rate
- pre-order/deposit count (if used)
- average order value proxy
- gross revenue
- gross margin
- blended acquisition cost proxy
Channel focus for research and recommendations:
- lightweight landing + form capture
- social DM/WhatsApp assisted sales
- marketplace trial listing (if appropriate)
- community/partner channels
Operating Rules
- Separate
observedvsinferredin every numeric section. - Use scenario ranges (
P10,P50,P90), never single-point certainty. - Prefer region-relevant recent sources; attach URL + access date.
- Do not invent competitor metrics when not available.
- Optimize recommendations for startup speed-to-revenue, not infrastructure perfection.
- Treat outputs as forecast proposals requiring operator approval.
Workflow
Stage 1: Intake, Freshness Check, and Clarify
- Capture required inputs.
- Check market-research freshness for the target business.
- If missing/stale, present filled Deep Research prompt from template and pause.
- Ask up to 3 high-impact clarification questions if blockers remain.
- Freeze an intake packet for this run.
Stage 2: Build Forecast Research Prompt
- Load
references/deep-research-prompt-template.md. - Fill placeholders from intake packet.
- Set
launch-surfaceexplicitly and enforce mode-specific metric guidance. - Keep expected outputs contract intact.
Stage 3: Run Forecast Research
- Run Deep Research using the filled forecast prompt (preferred).
- If Deep Research is unavailable, run equivalent manual web research with citation discipline.
- Collect evidence in a source table before forecasting.
Stage 4: Forecast and Goal Proposal
Produce:
- 90-day scenario forecast (
P10/P50/P90) using mode-appropriate metrics:- if
website-live: sessions, conversion rate, AOV, orders, gross revenue, gross margin, CAC, payback proxy - if
pre-website: qualified leads, assisted order attempts, assisted conversion, pre-orders/deposits (if used), AOV proxy, gross revenue, gross margin, acquisition cost proxy
- if
- competitor benchmark table (pricing, offer structure, channel posture)
- proposed targets for first 90 days and first 4 weeks
- assumption register with confidence tags (
high/medium/low) - risk register with mitigation actions
- week-1/week-2 validation checklist to recalibrate forecast quickly
Stage 5: Persist Outputs
Write:
docs/business-os/strategy/<BIZ>/<slug>-90-day-launch-forecast-vN.user.md
If this run is intended to seed SFS-00, also write:
docs/business-os/startup-baselines/<BIZ>/forecast-seed.user.md
If market research was refreshed in this run, also write:
docs/business-os/market-research/<BIZ>/<YYYY-MM-DD>-market-intelligence.user.md- Update:
docs/business-os/market-research/<BIZ>/latest.user.md
Render HTML companions (required for all .user.md files written/updated in this stage):
- Run:
pnpm docs:render-user-html -- <all .user.md output paths from this run>
- At minimum this includes:
- forecast file
- startup baseline seed file (if written)
- market-intelligence file (if written)
- market-intelligence latest pointer (if updated)
Output Contract
<slug>-90-day-launch-forecast-vN.user.md must include:
Executive Summary(<=12 bullets)Input Packet(what user provided)Launch Surface Mode(website-liveorpre-website) and chosen metric setCompetitor Benchmark Table(with source links)90-Day Forecast Table (P10/P50/P90)Proposed Outcome StatementProposed TargetsAssumptions RegisterRisk RegisterFirst-14-Day Validation PlanSource List(URL + access date)Confidence and Caveats
Definition of Expected Outcomes
An expected outcome in this skill means a measurable 90-day business result with:
Outcome statement: plain-language resultBaseline: starting point nowTarget: expected value or range by deadlineDeadline: exact dateLeading indicators: weekly signals proving progressDecision link: what decision this outcome unlocks
Use forecast evidence to propose these fields; do not leave them blank.
Integration with Existing Flow
- Forecasting runs before idea prioritization when startup context is sparse.
- Approved forecast outcomes seed SFS-00 business intent baseline.
lp-do-idea-generateshould use approved forecast outcomes as steering constraints.lp-do-fact-find/lp-do-analysis/lp-do-plan/lp-do-buildremain the delivery path for selected go items.
Red Flags (invalid output)
- Missing competitor citations for numeric claims.
- Single-point target claims without scenario ranges.
- No distinction between observed data and inferred estimates.
- No first-14-day validation plan.
- Proposed goals that ignore user budget/stock constraints.
- Uses website-only metrics in
pre-websitemode without conversion mapping. - Continues forecasting despite stale or missing market intelligence pack.
.user.mdoutputs were written but corresponding.user.htmlcompanions were not rendered.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
agent-ops-spec
Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.
agent-ops-state
Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.
agent-ops-spec
Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.
agent-ops-testing
Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.
agent-ops-testing
Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.
agent-ops-state
Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.
Didn't find tool you were looking for?