본문으로 건너뛰기
cccskills
무료GitHub에 공개

progressive-estimation

Estimate AI-assisted and hybrid human+agent development work with research-backed PERT statistics and calibration feedback loops

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

연동 도구Jira
설치 방법 보기

포함된 파일(1)

  • SKILL.md3.4 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Progressive Estimation

Estimate AI-assisted and hybrid human+agent development work using research-backed formulas with PERT statistics, confidence bands, and calibration feedback loops.

Overview

Progressive Estimation adapts to your team's working mode — human-only, hybrid, or agent-first — applying the right velocity model and multipliers for each. It produces statistical estimates rather than gut feelings.

When to Use This Skill

  • Estimating development tasks where AI agents handle part of the work
  • Sprint planning with hybrid human+agent teams
  • Batch sizing a backlog (handles 5 or 500 issues)
  • Staffing and capacity planning with agent multipliers
  • Release date forecasting with confidence intervals

How It Works

  1. Mode Detection — Determines if the team works human-only, hybrid, or agent-first
  2. Task Classification — Categorizes by size (XS–XL), complexity, and risk
  3. Formula Application — Applies research-backed multipliers grounded in empirical studies
  4. PERT Calculation — Produces expected values using three-point estimation
  5. Confidence Bands — Generates P50, P75, P90 intervals
  6. Output Formatting — Formats for Linear, JIRA, ClickUp, GitHub Issues, Monday, or GitLab
  7. Calibration — Feeds back actuals to improve future estimates

Examples

Single task:

"Estimate building a REST API with authentication using Claude Code"

Batch mode:

"Estimate these 12 JIRA tickets for our next sprint"

With context:

"We have 3 developers using AI agents for ~60% of implementation. Estimate this feature."

Best Practices

  • Start with a single task to calibrate before moving to batch mode
  • Feed back actual completion times to improve the calibration system
  • Use "instant mode" for quick T-shirt sizing without full PERT analysis
  • Be explicit about team composition and agent usage percentage

Common Pitfalls

  • Problem: Overconfident estimates Solution: Use P75 or P90 for commitments, not P50

  • Problem: Missing context Solution: The skill asks clarifying questions — provide team size and agent usage

  • Problem: Stale calibration Solution: Re-calibrate when team composition or tooling changes significantly

Related Skills

  • @sprint-planning - Sprint planning and backlog management
  • @project-management - General project management workflows
  • @capacity-planning - Team velocity and capacity planning

Additional Resources

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bouclem/skills72026년 5월 31일 업데이트

Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bouclem/skills72026년 5월 31일 업데이트

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bouclem/skills72026년 5월 31일 업데이트

Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bouclem/skills72026년 5월 31일 업데이트

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bouclem/skills72026년 5월 31일 업데이트

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bouclem/skills72026년 5월 31일 업데이트

bouclem의 스킬 모두 보기

이 스킬의 문제 신고