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deep-company-series

Write a publication-grade 8-part deep-dive series on a single company (~120k words total): cognitive reset / moat / profit engine / hidden assets / era variable (e.g. AI) / financials Buffett-style / management / valuation+redlines. The core IP is NOT writing but REVISING — a strict fact-check checklist catches pseudo-precision (probability-weighted expectations, third-party MAU discrepancies, linear extrapolation), absolute language, and cross-article number inconsistencies that most finance long-forms violate. Each piece stands alone but shares one valuation/management/price framework. Use when the user wants textbook-level depth on one company for public publishing (a single research report or earnings note is NOT this — use investment-research / earnings-review instead).

72

Quality

90%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-5

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

The body is an unusually actionable, well-sequenced workflow: concrete tools, thresholds, file paths, and validation gates for every phase, with no filler. Its main weaknesses are duplication between the pseudo-precision traps and the 7 revision checks, and a monolithic single-file layout that inlines the very checklist material (the skill's stated core IP) that belongs in a referenced file.

Suggestions

Move the detailed fact-check checklist and series/style tables into references/ (e.g. references/fact-check.md, references/series-template.md) and keep SKILL.md as an overview with one-level-deep, clearly signaled links.

De-duplicate section 4: state each pseudo-precision trap once and have the 7 revision checks reference it by name rather than restating it (e.g. checks 5 and 7 re-explain traps 1 and 3).

Trim the closing one-liner, which restates section 4 verbatim in spirit, to save tokens without losing guidance.

DimensionReasoningScore

Conciseness

The body is dense and operational (tables, checklists, tool commands) with almost no concept-explanation padding, but it is not fully lean: probability-weighted expectations and third-party-data caveats each appear twice (pseudo-precision traps vs. the 7 revision checks), and the closing one-liner restates section 4. It fits 'efficient; minor instances that could be trimmed' better than the perfectly lean anchor 5.

4 / 5

Actionability

Guidance is fully executable: named tool invocations with parameters (`report_audit` command=extract/verdict, `financial_rigor` command=cross_validate with a >1% deviation threshold), concrete output paths (`reports/{company}/《Understanding {company}》/0X-XX.md`), per-part word counts, title templates, a revision grading table, and grep patterns for absolute-language scans. Specific examples cover the common cases.

5 / 5

Workflow Clarity

Four clearly sequenced phases (research → write → cross-article scan → pre-publish gate) with explicit validation checkpoints (cross-validation of the same metric across articles, `report_audit` verdict as a PASS/FAIL gate), a revision-feedback loop with a verify-before-change step, and a cascade checklist after fixes. This matches the anchor's clear sequence + validation + feedback loops + checklist.

5 / 5

Progressive Disclosure

There is no bundle at all (no references/, scripts/, assets/), so the ~140-line body carries everything inline. Section headers are clear, but content that would naturally be a one-level-deep reference — the detailed fact-check checklist billed as the 'core IP', the banned-words and series-template tables — is inlined with no external navigation. This fits 'some structure but content that should be separate is inline' better than anchor 4.

3 / 5

Total

17

/

20

Passed

Description

92%Weight 40%Scale 1-5

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

A strong description: it states concrete capabilities (8 named series parts plus the revision/fact-check IP), gives an explicit 'Use when...' trigger with a negative boundary against adjacent skills, and keeps the language specific rather than hyped. The only gap is modest synonym coverage for natural user phrasings of company deep-dives.

Suggestions

Add one or two common user phrasings as triggers (e.g. 'stock deep dive', 'investment thesis series') to widen keyword coverage.

Keep watch that the long parenthetical enumeration of parts does not crowd out additional 'when' variations as the skill evolves.

DimensionReasoningScore

Specificity

The description lists multiple concrete, comprehensive actions: an 8-part series with named components ("cognitive reset / moat / profit engine / hidden assets / era variable... financials Buffett-style / management / valuation+redlines") plus a strict fact-check checklist enumerating specific pseudo-precision failures. It matches the anchor for multiple specific concrete actions with comprehensive coverage; nothing vague remains.

5 / 5

Completeness

It explicitly answers both what ("Write a publication-grade 8-part deep-dive series... The core IP is NOT writing but REVISING — a strict fact-check checklist...") and when ("Use when the user wants textbook-level depth on one company for public publishing"), with a concrete negative trigger boundary. This clearly matches the anchor 5 example structure.

5 / 5

Trigger Term Quality

Good natural keyword coverage — "deep-dive series", "one company", "textbook-level depth", "public publishing", "a single research report or earnings note" — but a few natural phrases users might say (e.g. "stock analysis", "investment thesis", ticker symbols) are missing. It sits above anchor 3 (missing variations) but below anchor 5's comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

Clear niche with explicit disambiguation — "a single research report or earnings note is NOT this — use investment-research / earnings-review instead" — so conflict risk with adjacent research skills is minimal. It outperforms anchor 4 ("minor overlap risk") because neighboring skills are actively ruled out.

5 / 5

Total

19

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
HKUDS/Vibe-Trading
Reviewed

Table of Contents

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