ai-collab-scorecard

name

ai-collab-scorecard

description

Evaluate long-term AI/Codex collaboration corpora, chat logs, prompts, revisions, and produced artifacts to build an evidence-grounded cognitive work capability scorecard. Use when the user asks to assess AI collaboration records, abstract a colleague's capability profile from their AI-assisted work, compare collaboration quality across people, or convert long-term chat/productivity traces into neutral dimension scores. Do not use as an IQ test, personality diagnosis, hiring decision automation, or clinical assessment.

AI Collab Scorecard

Purpose

Assess observable cognitive work behavior in long-term AI collaboration records. The result is a neutral scorecard, not an IQ estimate or total-person judgment.

Use references/setup-and-data-sources.md when installing, validating, or collecting historical collaboration records. Use references/sampling-protocol.md and scripts/build_corpus_manifest.py to freeze corpus scope. Use references/evidence-abstraction-pipeline.md and scripts/extract_collab_signals.py to turn raw history into five-dimension evidence. Use the detailed scoring anchors in references/rubric.md when assigning scores. Use references/output-schema.md for machine-readable output. Use references/worktypes.md and scripts/classify_worktype.py when deriving worktypes. Use references/share-card-image.md, references/share-copy.md, scripts/render_share_card_png.ps1, and scripts/render_share_card.py when the user asks for a shareable image/card. Use references/asset-production/worktype-avatar-brief.md only when producing or replacing the fixed worktype illustration asset batch outside runtime.

Respond in the user's language. For Chinese users, all human-facing conclusions, headings, score labels, dimension names, worktype descriptions, status notes, evidence bullets, limits, and share-card explanations must be in Chinese. Keep English only for machine-readable JSON keys, script arguments, file paths, fixed type_id values, or an optional appendix when cross-person comparison requires canonical English labels.

Interaction Mode

• Default to low-friction execution for end users: one consolidated permission request at most, concise progress, and one final Chinese result.
• For first-time install/run requests, prefer scripts/bootstrap.ps1 as the deterministic entrypoint. It handles repository acquisition, installation/update, environment diagnostics, validator repair, and render self-check before the assessment continues.
• If the user already grants network, write, install, validation, rendering, and read-only Codex/software history permissions in the same message, do not ask those permissions again except for platform-required tool approvals.
• Do not require a new Codex window after installation. If $ai-collab-scorecard is not discoverable in the current session, read the installed SKILL.md and references/setup-and-data-sources.md from disk and continue the same workflow in the current conversation.
• Suppress raw diagnostic JSON, install commands, repeated validation logs, and long environment explanations unless a check fails or the user asks for debug detail.
• Avoid duplicate conclusions: output one short self-check summary, then one complete scorecard. Do not repeat the same scores in multiple prose blocks unless it clarifies a decision.

Guardrails

• Score only evidence visible in the corpus: prompts, corrections, constraints, decisions, review comments, artifact changes, and follow-up results.
• Separate the user's contribution from the AI's contribution. A polished final artifact is weak evidence unless the corpus shows how the user steered, rejected, corrected, or validated it.
• Prefer "insufficient evidence" over confident inference when the corpus is thin, narrow, edited, or mostly AI-generated.
• Do not infer mental health, moral quality, personality type, or exact IQ. If asked about IQ, state that this skill measures AI-assisted cognitive work performance, not psychometric intelligence.
• Normalize by role, domain, task difficulty, and available context before comparing people.
• Keep the serious assessment and any playful/shareable derivative strictly separate. Humor may make the result easier to share, but must never change scores, evidence, confidence, or capability claims.

Workflow

0. If the user asks how to install, use, or reuse the skill, read references/setup-and-data-sources.md first:

   • For a new user, run scripts/bootstrap.ps1 when available instead of manually reproducing install steps
   • Verify the skill folder is installed under the active Codex skills directory
   • Run the basic skill validation when useful
   • Explain required runtime assumptions before collecting private history
   • If installation, validation, rendering, or corpus acquisition is incomplete on the current machine, enter the completion recovery protocol in references/setup-and-data-sources.md
   • Do not stop at the first missing dependency or configuration difference; diagnose the gap, ask for the minimum needed permission, repair what can be repaired, and continue toward a complete scorecard

1. Define the assessment scope and data source:

   • Person or team being assessed
   • Time window and corpus sources
   • Role/domain expectations
   • Whether the goal is self-review, coaching, team abstraction, or comparison
   • Corpus manifest or sampling method from references/sampling-protocol.md
   • If the user asks to use "all current Codex/software history" but has not explicitly granted local-history access in this turn, first ask for permission to inspect current Codex/software history read-only; do not assume elevated local access
   • After the user grants permission, treat the current local Codex history as the intended corpus: inspect only Codex-owned or clearly session-related history locations read-only, freeze a manifest, and do not require manual pasted logs unless the local history is unavailable
   • If the user asks for "all Codex history" without explicit authorization, use only user-authorized exports, visible conversation context, or explicitly allowed local history directories; do not silently scrape unrelated private app data

2. Build an evidence map:

   • Task types and difficulty
   • User prompts and constraints
   • AI outputs the user accepted, rejected, or revised
   • User corrections, tests, validations, and final artifacts
   • Repeated patterns across tasks
   • Use references/evidence-abstraction-pipeline.md for session segmentation, evidence tagging, and dimension mapping

3. Score the five minimum dimensions:

   • 问题定义与约束控制 (problem_framing)
   • 推理建模与机制抽象 (reasoning_modeling)
   • 证据校验与验证纪律 (evidence_verification)
   • AI 编排与批判性审阅 (ai_orchestration)
   • 交付转化与迭代沉淀 (delivery_iteration)

4. Produce dimension scores and a composite score:

   • Score each dimension as an integer from 0-100
   • Compute the weighted composite as an integer from 0-100 using references/rubric.md
   • Use the required component scoring standard in references/rubric.md; do not assign scores by overall impression
   • Use the repeatability worksheet in references/rubric.md before finalizing each score
   • Report percentile-like position only when a real comparison set exists; otherwise do not invent one
   • Include confidence for each score: low, medium, or high
   • Attach 2-5 concrete evidence bullets per dimension

5. Derive the worktype when requested or useful:

   • Use the full five-dimension score vector, not only the strongest dimension
   • Match against fixed prototypes in references/worktypes.md
   • Prefer scripts/classify_worktype.py to compute the primary type, fit score, secondary tendency, and risk modifiers from a score JSON
   • For Chinese reports, display serious_name_zh, share_name_zh, secondary_tendency_zh, risk_modifiers_zh, and band_zh; do not expose English enum values in the main conclusion
   • Do not invent new canonical type names

6. Explain the profile:

   • Strongest stable capability
   • Most important bottleneck
   • Best-fit collaboration pattern
   • Risks of over-inference
   • One or two next improvement levers

7. If the user asks for a shareable, viral, or image version, create a separate "Share Card" only after the serious scorecard:

   • Derive it from the scored dimensions and evidence, not from vibes.
   • Make it light, memorable, and mildly funny.
   • Keep it respectful and workplace-safe.
   • Avoid IQ claims, personality diagnosis, fixed identity claims, insults, medicalized labels, or hiring recommendations.
   • Include a tiny disclaimer that the card is a playful summary of the evidence-based scorecard.
   • If creating an image, select a confirmed pre-generated worktype illustration from assets/worktype-illustrations/manifest.json and render important text with a deterministic renderer.
   • Use scripts/render_share_card_png.ps1 for the final standard PNG card on Windows. It loads the deterministic contract from references/render-lock.json, uses the bundled pinned font from assets/fonts/NotoSansSC-VF.ttf, and fails if the font hash changes. Use scripts/self_check_render_determinism.ps1 after renderer or layout changes. Use scripts/render_share_card.py only for SVG debugging or editable inspection.
   • Use references/share-copy.json for standard Chinese share-card copy defaults and enforced text-length/layout limits.
   • Do not call an image model during scorecard/card generation. Image models may be used only when producing the fixed asset batch for the skill, following references/asset-production/worktype-avatar-brief.md.
   • When returning a rendered share card, include both a direct image file link and a direct link to the containing output directory.

Output Template

# AI 协作力评分卡

## 测评范围
- 测评对象：
- 语料范围：
- 角色/领域：
- 测评目标：
- 证据置信度：低/中/高

## 分数
| 维度 | 分数 /100 | 权重 | 置信度 | 解读 |
|---|---:|---:|---|---|
| 问题定义与约束控制 |  | 22 |  |  |
| 推理建模与机制抽象 |  | 22 |  |  |
| 证据校验与验证纪律 |  | 20 |  |  |
| AI 编排与批判性审阅 |  | 18 |  |  |
| 交付转化与迭代沉淀 |  | 18 |  |  |

综合分：`__/100`
档位：

## 评分拆解
每个维度保留以下拆解：
`核心行为 /45`、`跨任务一致性 /20`、`人类主导性 /20`、`难度迁移 /15`、`反例修正`、`上限限制`、`最终分`。

## 量化画像
- 五维向量：`[问题定义, 推理建模, 证据校验, AI 编排, 交付迭代]`
- AI 协作工作型：
- 工作型匹配度：`__/100`
- 第二倾向：
- 风险修正：
- 置信度：

## 各维度证据
## 能力画像
## 主要瓶颈
## 可迁移给同事的抽象
## 非声明与限制

Scoring Discipline

When evidence is mixed, score the repeated pattern, not the best sample. When task difficulty differs sharply, report separate sub-scores by task class instead of forcing one number.

Do not hide uncertainty inside smooth prose. A useful scorecard is allowed to say: "high output quality, but low confidence in human contribution because the corpus lacks correction traces."

Optional Share Card

Use a share card only as a derivative layer. It should be fun to read, but it is not the source of truth. For a standard final image on Windows, use scripts/render_share_card_png.ps1 with a JSON result that follows references/output-schema.md. For SVG debugging or editable inspection, use scripts/render_share_card.py. Both renderers select confirmed assets from assets/worktype-illustrations/manifest.json; deterministic placeholder art is only a draft fallback until the fixed image batch is approved.

# AI 协作工作型分享卡

类型：
含义：
这个人倾向于如何使用 AI：
高光能力：
喜剧化风险：
最适合的 AI 配置：
严肃评分卡链接：
小字声明：

Good share-card names describe work behavior, not inner essence. Prefer names like "Problem Architect", "Evidence Auditor", "Workflow Blacksmith", "Prompt Air-Traffic Controller", or "Scope Boundary Patrol". Avoid names that imply intelligence rank, moral worth, mental health, or permanent personality.