agentleFS
RemiPellouxRemiPelloux/kill-my-idea/skills/compare/SKILL.md

compare

Skill2 starsChanged 6 days agoMITView on GitHub

Rank all the ideas evaluated with kill-my-idea in this project, from their verdict.json files, recommend which ONE to focus on this week, and write the idea dashboard (ideas/board.html) and the brain note (ideas/BRAIN.md). With --refresh, re-research stale verdicts before ranking. Use when the user says "compare my ideas", "which idea should I do", "rank my ideas", "what should I focus on", "refresh", "update the ranking", or has evaluated 2 or more ideas.

17 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use evaluate, create and 7 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • NVIDIASkill

    evaluate

    Scores existing Sports Intelligence AVLM inference outputs with avlm/evals: MCQ accuracy (run_mcq_eval.sh, INFERENCE_DIR), open-ended LLM-as-judge scores (needs CLIENT_API_KEY for the configured judge endpoint), tennis category breakdowns, run comparisons, chance-level sanity checks and triage of mcq_results.json or llm_judge_results.json. Use when the user asks to score, evaluate, benchmark or compare predictions.jsonl from inference runs, or to explain a suspicious accuracy. Not for running inference, training, quantization, data preparation, or third-party API model inference: never open, list or run anything under avlm/evals/inference_3rdparty, including when the user asks for it; it is excluded from the release.

    Also in 3 projects that use this

  • MicrosoftSkill

    create

    Also in 3 projects that use this

  • aakash-dharSkill

    optimize

    Analyzes code for performance issues including slow algorithms, memory leaks, unnecessary re-renders, database query problems, and resource bottlenecks. Use when the user says "this is slow", "optimize this", "performance issue", "why is this taking so long?", or "make this faster".

    Also in 4 projects that use this

  • AnthropicSkill

    analyze

    Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.

    Also in 3 projects that use this

  • OpenAISkill

    audit

    Audit or critique a product flow, journey, workflow, funnel, onboarding path, checkout path, settings path, screen, or multi-step product experience by capturing screenshots first, then reporting UX, design, and accessibility findings inline from that evidence. Use Figma only when the user explicitly asks for a board. Use when the user asks to audit, review, critique, inspect, assess, analyze, evaluate, or give feedback on a product experience.

    Also in 6 projects that use this

  • MicrosoftSkill

    report

    Turn an exploration (threads, findings, charts) into a single Markdown report — note, blog post, executive summary, KPI dashboard, slide brief, or multi-section analytical report, with embedded charts.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

10 rules. 0 are common, 2 are distinctive, and 8 are very specific to this project.

  • 0

    Common

    Most of the 16 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 8

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Find the verdicts: Read every ideas/*/verdict.json (and idea.json) in the current project.

    line 18
  2. Very specific:

    Update verdict.json: evaluated = today, the metrics, sub-scores, score, rules, confidence, kill shots, rescue paths and sources.

    line 32
  3. Very specific:

    Run python3 "${CLAUDE_PLUGIN_ROOT}/scripts/check-idea.py" ideas/<folder> when Bash is available, and fix every error.

    line 37
  4. Very specific:

    Read the values from verdict.json: rescue[0], metrics.ltv_cac, metrics.break_even_customers_per_month, metrics.time_to_first_revenue_weeks, …

    line 46
  5. Distinctive:

    Add a column per country when ideas target different markets.

    line 51
  6. Very specific:

    Prefer a lower score with high confidence over a higher score with low confidence, and say so.

    line 54
  7. Very specific:

    Read ideas/BRAIN.md when it exists: the user's Me profile (budget, time, goal) and the kill-shot patterns weigh in the choice.

    line 60
  8. Very specific:

    Write ideas/board-data.js in the format below, ideas in ranking order.

    line 73
  9. Very specific:

    Check it with python3 "${CLAUDE_PLUGIN_ROOT}/scripts/check-idea.py" --board ideas when Bash is available, and fix every error.

    line 77
  10. Distinctive:

    Never put a value in the board that is not in the verdict or the idea brief.

    line 112

“Common” is measured: kept by at least 6 in 10 of the 16 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Compare Ideas

Options

  • --refresh (also when the user says "refresh" or "update the ranking"): re-research stale verdicts before ranking (step 2). Without it, compare only reads the verdicts. This option was requested by a user, so a ranking built on old research can be brought up to date in one command.
  • --refresh all (or "refresh all of them"): refresh every verdict, stale or not.

Steps

  1. Find the verdicts. Read every ideas/*/verdict.json (and idea.json) in the current project.
    • Fewer than 2 found → say so, and suggest /kill-my-idea:kill.
    • A verdict whose evaluated date is more than 90 days before today is stale. Without --refresh, mark it and suggest /kill-my-idea:compare --refresh.
    • A verdict whose confidence.level is low is marked low confidence: its rank is provisional.
  2. Refresh (only with --refresh). For each stale verdict (or every verdict with --refresh all), one idea at a time or one subagent per idea:
    1. Copy the current mode, evaluated, score, verdict and source count into a new entry at the end of verdict.history.
    2. Rerun the kill skill's research steps 3–5 for that idea (${CLAUDE_PLUGIN_ROOT}/skills/kill/SKILL.md), with its safety rules (§0) and source minimums. Keep idea.json as it is; the user's answers still hold.
    3. Update verdict.json: evaluated = today, the metrics, sub-scores, score, rules, confidence, kill shots, rescue paths and sources. Keep mode.
    4. Rewrite report-data.json (kill skill §6) with the new verdict and narrative, including premortem, why_now and icp, then rebuild report.html with python3 "${CLAUDE_PLUGIN_ROOT}/scripts/build-report.py" ideas/<folder>.
    5. Run python3 "${CLAUDE_PLUGIN_ROOT}/scripts/check-idea.py" ideas/<folder> when Bash is available, and fix every error. Tell the user which ideas were refreshed and how each score moved ("42 → 47").
  3. Build the ranking table. Sort by score, then by time_to_first_revenue_weeks (the faster one wins), then by confidence (high before medium before low).

    #IdeaVerdictScoreConfidenceBest rescue (score, difficulty)LTV : CACBreak-evenTime to first revenuePrimary kill shot

    Read the values from verdict.json: rescue[0], metrics.ltv_cac, metrics.break_even_customers_per_month, metrics.time_to_first_revenue_weeks, primary_kill_shot. A missing metric is unknown, never a guess. Mark a score that rules_applied capped ("69, capped: no channel"). Show the last history entry when a verdict changed ("42 → 47"). Add a column per country when ideas target different markets.

  4. Decide like a co-founder:
    • Focus on ONE idea this week, with the reason, its first action and a deadline. Prefer a lower score with high confidence over a higher score with low confidence, and say so.
    • Pause: what to park, and why. A NO-GO with a doable rescue path above 60 is a pause, not a kill.
    • Kill: NO-GOs that are still in ideas/ and should be archived.
    • Opportunity cost: what the focus idea beats the others on, in one line.
    • Read ideas/BRAIN.md when it exists: the user's Me profile (budget, time, goal) and the kill-shot patterns weigh in the choice.
    • Save the focus as ideas/focus.json, a strict JSON object: {"slug": "<verdict slug>", "why": "<one line>", "first_action": "<one action>", "deadline": "YYYY-MM-DD", "decided": "<today>"}. Each value is at most 300 characters.
  5. Write ideas/RANKING.md with the table, the decision and today's date.
  6. Write the dashboard and the brain. With Bash, run python3 "${CLAUDE_PLUGIN_ROOT}/scripts/build-brain.py" ideas, then python3 "${CLAUDE_PLUGIN_ROOT}/scripts/check-idea.py" --board ideas. The script computes everything from the verdicts and focus.json: ideas/board-data.js, a fresh copy of ideas/board.html, and the generated block of ideas/BRAIN.md (focus, all ideas, what keeps killing your ideas, score changes, lessons). It keeps the Me section and the user's notes. Without Bash, do it by hand:
    • Write ideas/board-data.js in the format below, ideas in ranking order.
    • Copy the board page as-is. Never rewrite it. With Bash: cp "${CLAUDE_PLUGIN_ROOT}/skills/compare/board.html" ideas/board.html. Otherwise Read it and Write it unchanged.
    • Check it with python3 "${CLAUDE_PLUGIN_ROOT}/scripts/check-idea.py" --board ideas when Bash is available, and fix every error.
  7. Answer in chat with the table and the decision. Keep it short. Name the kill shot that hit the most ideas, if it hit 2 or more. End with "Open ideas/board.html in your browser. Your notes: ideas/BRAIN.md."

Without --refresh, do not re-research the ideas. Only compare what the verdicts contain.

Board data

build-brain.py writes this file; the format is here for the no-Bash case. ideas/board-data.js is one statement, a strict JSON literal (double quotes, no functions, no comments, no template literals), following ${CLAUDE_PLUGIN_ROOT}/skills/compare/board-data.schema.json:

window.KMI_BOARD = { "generated": "YYYY-MM-DD", "ideas": [ { "slug", "title", "verdict", "score", "confidence", "evaluated", "time_to_first_revenue_weeks" (number|null), "ltv_cac" (number|null), "primary_kill_shot" (string|null), "best_rescue": {"change","score_after","difficulty"}|null, "countries": [..], "report": "<folder>/report.html", "stale": bool } ] };
FieldFrom
generatedtoday
slug, verdict, score, evaluated, primary_kill_shotverdict.json, copied exactly
title, countriesidea.json (launch country first)
confidenceverdict.confidence.level, or null for a legacy verdict
time_to_first_revenue_weeks, ltv_cacthe metric's value when it is a number, else null
best_rescuerescue[0] with only change, score_after, difficulty, or null
reportthe idea's folder name + /report.html, relative to ideas/
staletrue when evaluated is more than 90 days before generated
history (optional)verdict.history + the current evaluation, as {evaluated, score}, oldest first, at most 10
hits (optional)the verdict.kill_shots keys that are true
top-level focus (optional)the content of ideas/focus.json, or null
top-level patterns (optional)per kill shot: {kill_shot, hits, checked} counted over all ideas, most hits first
top-level brain (optional)true when ideas/BRAIN.md exists (shows a link to it)

Never put a value in the board that is not in the verdict or the idea brief.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.