Platform Arbitrage
acogood/diffmode_free
Platform arbitrage audit for a founder's product — a Diffmode growth-tactics think-tank research stage (prompt TT-DG-003).
Generates structured pivot options for a scored idea based on weak dimensions, marketinsights signals, and founder constraints.
$ npx skills add MaxKmet/idea-validation-agents --skill pivot-engine -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install MaxKmet/idea-validation-agents pivot-engine --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pivot-engine .claude/skills/pivot-engine && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "pivot-engine" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pivot-engine into .claude/skills/pivot-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pivot-engine", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pivot-engineType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add MaxKmet/idea-validation-agents --skill pivot-engine -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install MaxKmet/idea-validation-agents pivot-engine --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/pivot-engine .agents/skills/pivot-engine && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pivot-engine" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pivot-engine into .agents/skills/pivot-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pivot-engine", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add MaxKmet/idea-validation-agents --skill pivot-engine -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install MaxKmet/idea-validation-agents pivot-engine --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/pivot-engine .cursor/skills/pivot-engine && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "pivot-engine" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pivot-engine into .cursor/skills/pivot-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pivot-engine", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/MaxKmet/idea-validation-agents.git --path skills/pivot-engine--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add MaxKmet/idea-validation-agents --skill pivot-engine -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install MaxKmet/idea-validation-agents pivot-engine --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/pivot-engine .gemini/skills/pivot-engine && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "pivot-engine" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pivot-engine into .gemini/skills/pivot-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pivot-engine", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install MaxKmet/idea-validation-agents pivot-engineInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add MaxKmet/idea-validation-agents --skill pivot-engine -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/pivot-engine .github/skills/pivot-engine && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "pivot-engine" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pivot-engine into .github/skills/pivot-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pivot-engine", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add MaxKmet/idea-validation-agents --skill pivot-engine -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install MaxKmet/idea-validation-agents pivot-engine --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/pivot-engine .opencode/skills/pivot-engine && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "pivot-engine" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pivot-engine into .opencode/skills/pivot-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pivot-engine", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
pivot-engineGenerates structured pivot options for a scored idea based on weak dimensions, marketinsights signals, and founder constraints.
Pivot Engine is an agent skill from MaxKmet/idea-validation-agents. Generates structured pivot options for a scored idea based on weak dimensions, marketinsights signals, and founder constraints. Includes scoring simulation, minimum viable pivot criteria, effort estimation, and indie buildability filtering.
Its SKILL.md is about 4.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It works with Reddit and TikTok. The repository describes itself as: AI agents that act as your personal venture analyst - from startup idea brainstorming to full validation and go-to-market strategy. Built for developers who'd rather validate in… The licence is MIT.
9 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 3a4c800. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are json and markdown).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Pivot Engine loads about 4.7k tokens when it runs. Until then it costs about 64 tokens; SKILL.md has 1,799 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from MaxKmet/idea-validation-agents at commit 3a4c800, republished under its MIT licence (© MaxKmet). 1,799 words, ~4,691 tokens.
.claude/skills/pivot-engine/SKILL.md (or your agent's skills folder).<!-- version: 0.3.0 | outputs: memory/ideas/<slug>/pivot_options.json + memory/ideas/<slug>/pivot_report.md -->
When an idea scores poorly in one or more dimensions, generate concrete pivot options rather than abandoning the idea entirely. A pivot is a deliberate change in one variable to improve the weakest dimension — not a complete restart. The best pivots preserve what's already strong while fixing what's broken, and they're grounded in real market signals, not wishful thinking.
memory/ideas/<slug>/scores.json (current scores — required)memory/ideas/<slug>/weaknesses.json (weak dimensions with root causes — required, run weakness-detection first)memory/ideas/<slug>/idea.md (current concept)memory/ideas/<slug>/competitors.json (positioning gaps, competitor complaints)memory/ideas/<slug>/distribution.json (current channel assessment)memory/ideas/<slug>/pricing.json (current pricing model and WTP)memory/ideas/<slug>/retention.json (current retention assessment)memory/user_profile.md (ICP tier, budget, time, distribution advantages)memory/market_insights/<niche>-*-<YYYY>-<MM>.md (trend data — use all available platform files)Market insights are essential for generating pivots grounded in real demand rather than theory. Extract:
| Field | How it informs pivots |
|---|---|
top_signals | Identify adjacent niches or audience segments with validated demand. A trending subreddit or TikTok hashtag near the idea's space suggests a viable audience pivot target. |
trend_velocity | If the current niche is "declining", pivoting within it is futile — pivot to an adjacent rising niche. If "rising-fast", the problem may be execution (distribution, pricing), not the market. |
monetization_evidence | If monetization is weak but market_insights show competitors successfully charging in the space, the issue is pricing model or positioning, not WTP. Pivot the model, not the idea. |
| Platform narratives (Reddit complaints, TikTok trends, App Store gaps) | Source specific pivot targets: underserved segments mentioned in Reddit threads, content angles trending on TikTok, App Store categories with stale top results. |
| Pivot type | What changes | What stays | When to use |
|---|---|---|---|
| Audience narrowing | Target user segment | Core problem and solution | Demand is broad but shallow; CAC too high because targeting is diffuse |
| Audience expansion | Broaden from niche to adjacent segment | Core solution mechanics | Market is too small (micro-niche verdict); SOM < $10K |
| Feature simplification | Scope and complexity | Core value proposition | Complexity too high; time-to-MVP exceeds founder capacity |
| Feature pivot | Core feature emphasis | Target audience and problem | Current feature set doesn't match what users actually want (review mining signals) |
| Niche pivot | Problem space subcategory | Solution approach | Competition too high in broad category; narrow to underserved niche |
| Pricing pivot | Pricing model or price point | Product and audience | WTP mismatch; wrong model for usage pattern; competitive pricing gap |
| Distribution pivot | Primary acquisition channel | Product and audience | Current channel is unviable; founder lacks skills/budget for assumed channel |
| Platform pivot | Target platform (iOS → web, mobile → desktop) | Core idea and audience | Wrong platform for audience behavior or market dynamics |
| Monetization pivot | Revenue model (B2C → B2C2B, app → content, product → service) | Core domain expertise | Direct monetization unviable but the domain has other revenue paths |
| Problem pivot | Which problem to solve | Target audience and domain | Audience is right but the specific pain point is weak; adjacent problem is stronger |
A pivot must be different enough to change the score but similar enough to preserve existing work and insights. Apply the Same Idea Test:
root_cause_type of "addressable" or "situational" in weaknesses.json. Pivots cannot fix structural weaknesses unless the pivot changes the core mechanic (which usually means it's a new idea).weaknesses.json. Identify all weak dimensions and their root causes.root_cause_type = "addressable" or "situational") → these are pivot targetsroot_cause_type = "knowledge-gap") → these need more research, not a pivotroot_cause_type = "structural") → only fixable by major pivot or new ideaoverall_weakness_severity = "fatal" and all weaknesses are structural, recommend dropping rather than pivoting. Note this in the output.Use this mapping to identify which pivot types are most likely to improve each weak dimension:
| Weak dimension | Primary pivot types | Secondary pivot types |
|---|---|---|
| Demand (< 40) | Problem pivot, audience narrowing | Niche pivot |
| Competition (< 40) | Niche pivot, audience narrowing | Feature pivot, platform pivot |
| Monetization (< 40) | Pricing pivot, monetization pivot | Audience narrowing (higher-WTP segment) |
| Distribution (< 40) | Distribution pivot, platform pivot | Audience narrowing (more reachable segment) |
| Retention (< 40) | Feature pivot, feature simplification | Problem pivot (pick a stickier problem) |
| Founder-Market Fit (< 40) | Niche pivot (toward founder's domain), feature simplification | Platform pivot (to founder's strongest platform) |
If multiple dimensions are weak, prioritize the one with the lowest score AND an addressable root cause.
For each applicable pivot type (from Step 2), generate a concrete option:
Pull evidence from market_insights:
competitors.jsonBe specific, not generic:
Generate 2–3 options, ranked by expected impact. Each option must pass the Minimum Viable Pivot Criteria.
For each pivot option, simulate the expected score change by projecting how each dimension would shift. This gives the orchestrator enough information to decide whether a full re-score is warranted.
For the dimension(s) the pivot targets, estimate the new score range based on:
idea-scoringFor dimensions the pivot doesn't target, assume they remain unchanged unless there's a clear secondary effect:
projected_score = sum of (projected_dimension_scores × weights) × projected_floor_penaltyUse the same weights and floor penalty logic from idea-scoring. This is an estimate — the actual re-score (step 4 of the pivot-optimization workflow) will be definitive.
Report as a range: projected_score_range: { low: X, high: Y }.
Each pivot has an execution cost. Estimate it relative to the founder's tier:
| Effort level | Definition | Typical timeline |
|---|---|---|
| Low | Can be done in a weekend. Changes copy, positioning, pricing, or targeting — no code changes. | 1–3 days |
| Medium | Requires feature changes or new content. Some development work. | 1–3 weeks |
| High | Significant rebuild. New core feature, new platform, or new audience requiring fresh research. | 1–3 months |
| Founder tier | Adjust |
|---|---|
| Beginner | Upgrade effort by one level (what's "medium" for a builder is "high" for a beginner) |
| Builder | No adjustment |
| Growth | Downgrade effort by one level (what's "medium" for a builder is "low" for growth) |
A pivot with "high" effort for the founder's tier should be flagged as risky — the time investment may not be justified unless the projected score improvement is substantial (≥ 20 points).
Before finalizing, verify each pivot option passes these constraints:
| Constraint | Fails if |
|---|---|
| Solo buildable | Pivot requires a team (e.g., marketplace requiring both supply and demand side simultaneously) |
| Budget feasible | Pivot requires spend exceeding founder's budget tier (e.g., "run paid social" for a Bootstrap founder) |
| Time feasible | Pivot requires > 3 months of work for the founder's tier |
| Skill feasible | Pivot requires skills the founder doesn't have and can't learn in 4 weeks (e.g., "build an ML model" for a no-code beginner) |
| No enterprise creep | Pivot moves the idea toward B2B enterprise, custom sales, or long sales cycles — fundamentally not an indie B2C play |
If a pivot fails any constraint, either modify it to fit or discard it and note why.
Rank remaining options by: (projected_score_improvement / effort_level) — the best pivot is the one with the highest score impact per unit of effort.
Tie-breakers:
Write two files to memory/ideas/<slug>/:
pivot_options.jsonMachine-readable structured data for downstream skills (idea-scoring, decision-memo):
{
"original_score": 0,
"original_verdict": "",
"triggered_by_weaknesses": [
{
"dimension": "",
"score": 0,
"root_cause_type": "",
"root_cause_description": ""
}
],
"structural_weaknesses_unpivotable": [],
"pivot_options": [
{
"pivot_id": "pivot-1",
"pivot_type": "",
"description": "",
"specific_change": "",
"evidence": "",
"evidence_source": "",
"meets_minimum_viable_pivot": true,
"scoring_simulation": {
"dimensions_improved": [
{ "dimension": "", "current": 0, "projected_low": 0, "projected_high": 0 }
],
"dimensions_worsened": [
{ "dimension": "", "current": 0, "projected_low": 0, "projected_high": 0 }
],
"dimensions_unchanged": [],
"projected_score_range": { "low": 0, "high": 0 },
"projected_verdict_range": ""
},
"effort": {
"level": "low | medium | high",
"tier_adjusted_level": "low | medium | high",
"timeline": "",
"what_changes": "",
"what_stays": ""
},
"indie_buildability": {
"passes": true,
"constraints_checked": ["solo_buildable", "budget_feasible", "time_feasible", "skill_feasible", "no_enterprise_creep"],
"failed_constraints": []
},
"trade_offs": [],
"variables_changed": 0
}
],
"recommended_pivot": "",
"recommended_pivot_rationale": "",
"drop_recommendation": false,
"drop_rationale": "",
"market_insights_sources_used": []
}pivot_report.mdHuman-readable pivot brief. This is the document the founder actually reads. Write it after pivot_options.json is complete — source all data from the JSON, don't introduce new judgements.
---
idea_slug: <slug>
original_score: <X>
original_verdict: <verdict>
recommended_pivot: <pivot_id>
created_at: <YYYY-MM-DD>
---
# Pivot Report: <Idea Name>
## Why the Original Idea Scored <X>/100
<2–3 sentences. State the root cause of the low score plainly — not a list of every problem, just the one or two structural reasons the idea can't work as-is. Cite specific scores from scores.json (e.g. "Distribution scored 34/100 because…"). No hedging.>
---
## What Can Be Fixed vs. What Can't
**Addressable weaknesses** (pivot targets):
- <dimension>: <1-sentence root cause and why it's fixable>
- ...
**Structural weaknesses** (cannot be pivoted away):
- <dimension>: <1-sentence root cause and why no pivot can fix it>
- ...
<If all weaknesses are structural, state clearly that a pivot is unlikely to rescue this idea and explain why.>
---
## Pivot Options
### Option 1 — <Pivot Type>: <Short Name> · Projected score: <low>–<high>/100 · Effort: <tier_adjusted_level>
**The change:** <1–2 sentences. Be specific about exactly what changes — audience, feature, channel, pricing model, platform. Name concrete details (specific subreddits, competitor pricing, App Store keywords, etc.).>
**Why this works:** <2–3 sentences grounded in evidence. Cite the market_insights signal, competitor gap, or user complaint that supports this direction. Name the source (e.g. "r/FigmaDesign has 150K active members discussing invoice pain", "BookPal's 1-star reviews consistently mention X", "TikTok hashtag #X has 40M views and rising velocity").>
**What stays the same:** <1 sentence. What existing work and strengths are preserved.>
**Score projection:**
| Dimension | Current | Projected |
|---|---|---|
| <dimension> | <current>/100 | <low>–<high>/100 |
| <dimension (worsened)> | <current>/100 | <low>–<high>/100 |
| ... | ... | unchanged |
**Trade-offs:** <1–2 sentences. What this pivot gives up. Be honest — every pivot has a cost.>
**Effort:** <What specifically needs to change — code, copy, positioning, research. Timeline.>
---
### Option 2 — <Pivot Type>: <Short Name> · Projected score: <low>–<high>/100 · Effort: <tier_adjusted_level>
<Same structure as Option 1.>
---
### Option 3 — <Pivot Type>: <Short Name> · Projected score: <low>–<high>/100 · Effort: <tier_adjusted_level> *(optional)*
<Same structure as Option 1. Include only if a genuinely distinct third option exists.>
---
## Recommendation
**Go with Option <N> — <Short Name>.**
<3–4 sentences. State why this option has the best impact-to-effort ratio. Reference the scoring simulation. Name the one thing that makes this pivot more credible than the alternatives (the market signal, the competitor gap, the distribution advantage). End with a specific first action the founder should take this week.>
**If this pivot also scores below 50:** <1 sentence — what that means and what to do (drop, major rethink, or new idea slug).>
---
## What to Do First
<1–3 concrete steps, ordered. Each step should be doable within a week. No vague advice — name the specific subreddit, pricing change, App Store keyword, or feature to cut. If a RAT experiment makes sense before committing to the pivot, define it here: ≤2 weeks, ≤$100, pass/fail criteria.>pivot_report.md62–71/100 is honest. 67/100 implies certainty the model doesn't have.overall_weakness_severity = "fatal" and all weaknesses are structural, set drop_recommendation = true and explain why no pivot can save this idea. Still generate 1 option as a "Hail Mary" if the founder wants to try, but be honest about the odds.competitors.json for positioning_gaps — an identified gap with evidence is the strongest pivot foundation.pivot_id field is used by idea-scoring to link re-scores in pivot_scores.json back to the specific option.stale_after date, note that pivot evidence may be outdated and recommend refreshing trend analysis before committing to a pivot direction.© MaxKmet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/pivot-engine of MaxKmet/idea-validation-agents.
Open the folder on GitHubat commit 3a4c800
Pivot Engine next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Pivot Engine this skillMaxKmet/idea-validation-agents | 474 | — | ~4.7k | Automated safety check: Pass | MIT | |
| Platform Arbitrageacogood/diffmode_free | 163 | — | ~2.5k | Automated safety check: Pass | Apache-2.0 | |
| Social Media Monitornexscope-ai/eCommerce-Skills | 1.1k | — | ~584 | Automated safety check: Pass | MIT | |
| Last30daysmvanhorn/last30days-skill | 64k | — | ~7.8k | Automated safety check: Notes | MIT | |
| Scrapecreators APIScrapeCreators/social-media-research-skills | 3.3k | 1 repos | ~4k | Automated safety check: Notes | MIT | |
| Last30days CnJesseovo/last30days-skill-cn | 1.9k | — | ~2.4k | Automated safety check: Notes | MIT |
acogood/diffmode_free
Platform arbitrage audit for a founder's product — a Diffmode growth-tactics think-tank research stage (prompt TT-DG-003).
nexscope-ai/eCommerce-Skills
Monitor social media mentions, trends, and competitor activity for e-commerce brands.
mvanhorn/last30days-skill
Research what people actually say about any topic in the last 30 days.
ScrapeCreators/social-media-research-skills
Scrape and extract public data from 27+ social media platforms using the ScrapeCreators REST API.
Jesseovo/last30days-skill-cn
Research what Chinese internet users actually said in the last 30 days across Weibo, Xiaohongshu (RED), Bilibili, Zhihu, Douyin, WeChat public accounts, Baidu and Toutiao: engagement-weighted…
AgriciDaniel/claude-ads
Runs a source-grounded paid advertising audit across up to 12 ad platforms, with parallel platform workers, deterministic scoring and a versioned JSON bundle.
MaxKmet/idea-validation-agents
Models LTV, CAC by channel, LTV:CAC ratios, and payback period for an indie developer.
MaxKmet/idea-validation-agents
Maps the full competitive landscape — direct, indirect, substitute, and emerging competitors — with positioning gap analysis, review mining, and marketinsights-calibrated saturation scoring.
MaxKmet/idea-validation-agents
Writes a concise, human-readable decision brief summarizing the full validation analysis — including score, verdict, RAT experiment, pre-mortem, and tier-appropriate next actions.
MaxKmet/idea-validation-agents
Scores the strength of core human desire motivations (survival, status, belonging, control, curiosity) for a given app idea to predict user pull and retention potential.
MaxKmet/idea-validation-agents
Evaluates organic reach potential, paid feasibility, platform distribution advantages, creator economy fit, and founder edge for a B2C app idea.
MaxKmet/idea-validation-agents
Aggregates all dimension scores into a final idea score (0–100) and issues a verdict.
Generates structured pivot options for a scored idea based on weak dimensions, marketinsights signals, and founder constraints. Pivot Engine is an agent skill from MaxKmet/idea-validation-agents. Generates structured pivot options for a scored idea based on weak dimensions, marketinsights signals, and founder constraints.
Run `npx skills add MaxKmet/idea-validation-agents --skill pivot-engine -a claude-code`. Or copy the skill folder (skills/pivot-engine in MaxKmet/idea-validation-agents) into .claude/skills/pivot-engine in your project. Claude Code loads it when a task matches its description.
Run `npx skills add MaxKmet/idea-validation-agents --skill pivot-engine -a codex`. Or copy the skill folder (skills/pivot-engine in MaxKmet/idea-validation-agents) into .agents/skills/pivot-engine in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add MaxKmet/idea-validation-agents --skill pivot-engine -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pivot-engine, .gemini/skills/pivot-engine, .github/skills/pivot-engine and .opencode/skills/pivot-engine in your project.
SKILL.md names no scripts, command-line tools or credentials: Pivot Engine is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Pivot Engine is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.7k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Pivot Engine: Platform Arbitrage (acogood/diffmode_free, 163 stars), Social Media Monitor (nexscope-ai/eCommerce-Skills, 1.1k stars), Last30days (mvanhorn/last30days-skill, 64k stars) and Scrapecreators API (ScrapeCreators/social-media-research-skills, 3.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
MaxKmet (a GitHub user) maintains it in MaxKmet/idea-validation-agents, which has 474 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on June 16, 2026.
Source: MaxKmet/idea-validation-agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.