CtrlK
BlogDocsLog inGet started
Tessl Logo

earnings-team

AI Berkshire skill: 财报精读团队:四大师并行解读 + 公众号发布. Source: skills/earnings-team.md.

56

Quality

63%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./codex-skills/earnings-team/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is a well-structured, highly actionable multi-agent workflow with concrete commands, templates, and an explicit validation exit-gate. Its main weakness is monolithic inline content with no progressive disclosure via reference files, plus minor verbosity in the framing sections.

Suggestions

Split per-agent prompts (Agents 1–6) and output templates into reference files referenced one level deep from SKILL.md to reduce the monolithic body.

Trim the '设计理念' preamble and repeated role restatements/motivational quotes to tighten token efficiency.

Keep the strong 准出/打回 feedback loop but consider adding an explicit per-agent validation step before the Team Lead synthesis checkpoint.

DimensionReasoningScore

Conciseness

The body is largely efficient with concrete tables, agent prompts, and commands, but pads with a design-philosophy preamble and restated roles/motivational quotes that could be trimmed.

2 / 3

Actionability

Concrete executable bash commands (financial_rigor.py cross-validate/verify-valuation, report_audit.py), named agents with specific tasks, exact output templates, and input-format examples make the guidance copy-paste ready.

3 / 3

Workflow Clarity

A clear three-stage sequence (研究→合成→发布) with numbered steps, a progress-tracking template, and an explicit validation exit-gate (准出/打回 feedback loop) provides strong checkpoints for error recovery.

3 / 3

Progressive Disclosure

No bundle files exist and the ~460-line body is a single monolithic document with everything inline; well-sectioned, but per-agent prompts and output templates would be better split into reference files.

2 / 3

Total

10

/

12

Passed

Description

50%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description conveys the skill's domain and two main actions but is generic and omits any explicit usage trigger, leaving 'when to use' only implied. It also does not distinguish the skill from closely related sibling skills.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when 精读重要公司的关键财报并产出可发布的公众号文章'.

Replace the meta prefix 'AI Berkshire skill:' and the source citation with capability-focused language and add trigger variations (季报/业绩/电话会/年报).

Differentiate from siblings, e.g. 'Use for 重要公司关键财报的六Agent团队深度精读+发布;快速单视角过一遍请用 /earnings-review'.

DimensionReasoningScore

Specificity

The phrase '财报精读团队:四大师并行解读 + 公众号发布' names the earnings domain and two actions (parallel reading, public-account publishing), but is not comprehensive and the 'AI Berkshire skill:' prefix is meta-labeling rather than a capability.

2 / 3

Completeness

It states what the skill does but never says when to use it; per the guidelines, a missing 'Use when...' clause caps completeness at 2.

2 / 3

Trigger Term Quality

Core natural terms (财报, 精读, 公众号) are present, but common variations (季报, 业绩, 电话会) are missing and there is no explicit trigger clause.

2 / 3

Distinctiveness Conflict Risk

The four-master framing is fairly niche, but the description does not differentiate it from overlapping sibling skills like /earnings-review and /investment-team mentioned in the body.

2 / 3

Total

8

/

12

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
xbtlin/ai-berkshire
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.