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canslim-screener

Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system.

Quality

74%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/canslim-screener/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

60%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 highly actionable — concrete commands, flags, API-budget math, and a clearly sequenced six-step workflow backed by a real, complete bundle with well-signaled references. Its main weakness is token efficiency: substantial duplication across sections (prerequisites, rating bands, report structure, Finviz fallback explained repeatedly) and detail inlined that the reference files or scripts already carry.

Suggestions

Deduplicate repeated content: keep the prerequisites/API-key/install instructions in one place (currently verbatim in both 'Prerequisites' and 'Step 1'), and state the rating bands once (currently in both 'Output' and 'Step 5').

Delete or drastically shrink the ~60-line Step 6 report template — report_generator.py already produces that exact format — keeping only the summary essentials Claude must add (interpretation, recommendations, risk factors).

Move the per-calculator scoring thresholds (the calculators subsection, e.g. 'Scoring: 50%+ = 100pts, 30-49% = 80pts') into references/scoring_system.md, which already documents them, and cut the '7 of 7, 100% coverage' restatements (~10 occurrences) to one.

DimensionReasoningScore

Conciseness

Noticeably verbose through duplication rather than explanation of known concepts: prerequisites and the pip-install/API-key instructions appear verbatim in both "Prerequisites" and "Step 1"; rating bands are listed twice ("Output" and "Step 5"); report structure is repeated in "Output" and "Step 4"; the Finviz fallback is explained four times; and the "7 of 7 / 100% coverage" claim is restated roughly ten times — several padded sections, matching the level-2 anchor rather than the 'some unnecessary explanation' level 3.

2 / 5

Actionability

Mostly executable, copy-paste-ready guidance: concrete commands ("pip install requests beautifulsoup4 lxml", "python3 screen_canslim.py --api-key $FMP_API_KEY --max-candidates 35"), documented flags (--rs-benchmark, --disable-rs, --top, --output-dir), exact API-budget arithmetic (35 × 7 + 3 = 248 calls), and symptom/solution troubleshooting pairs. Minor gaps keep it below fully-executable: inconsistent path conventions ("python3 skills/canslim-screener/scripts/screen_canslim.py" vs "cd skills/canslim-screener/scripts") and mixing env-var vs --api-key key passing.

4 / 5

Workflow Clarity

A clear six-step sequence (verify API access → determine universe → execute → parse results → analyze → report) with explicit error-recovery paths in the Troubleshooting section (429 auto-retry, Finviz graceful degradation, missing libraries). It sits just below the top anchor because checkpoints are partly implicit — no explicit step validates the run succeeded (e.g., check exit status or that the JSON output file exists) before Step 4 reads it — but it is clearly above the 'validation gaps / implicit checkpoints' level 3.

4 / 5

Progressive Disclosure

Scored against the actual bundle: every referenced path exists (4 references/*.md files, 6 top-level scripts, 7 calculators — all match the body's citations), references are one level deep and exceptionally well-signaled with sizes, purpose statements, and a "How to Use References" reading order. Slightly below the top anchor because meaningful content is inlined that belongs in those references: per-calculator scoring thresholds duplicate references/scoring_system.md, the ~60-line Step 6 report template duplicates what report_generator.py already emits, and the Resources section re-documents each script's methods.

4 / 5

Total

14

/

20

Passed

Description

78%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 that explicitly answers both what and when, with concrete CANSLIM-specific trigger phrases and good natural-keyword coverage. The main gaps are minor: only one concrete action (screening) is named despite the skill also scoring, ranking, and generating reports, and a few obvious synonym triggers are absent.

DimensionReasoningScore

Specificity

The one concrete action is "Screen US stocks using William O'Neil's CANSLIM growth stock methodology" — it names the domain and a single clear action but does not enumerate the several operations the skill actually performs (component scoring, ranking, report generation), matching the '1-2 concrete actions' anchor rather than the 'several specific actions' anchor.

3 / 5

Completeness

Explicitly answers both questions: what ("Screen US stocks using William O'Neil's CANSLIM growth stock methodology") and when ("Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum") with concrete trigger phrases — a clear match for the top anchor, not merely the 'when could be more specific' level below.

5 / 5

Trigger Term Quality

Natural trigger phrases like "CANSLIM stock screening", "growth stock analysis", "momentum stock identification", and "strong earnings and price momentum following O'Neil's investment system" give good keyword coverage, though common variants such as "CAN SLIM" (spaced), "IBD-style", or simply "O'Neil screener" are missing — a few natural terms short of the comprehensive anchor.

4 / 5

Distinctiveness Conflict Risk

The CANSLIM/O'Neil framing carves out a clear niche with minimal wrong-skill trigger risk, but the broader phrases "growth stock analysis" and "momentum stock identification" could overlap with generic growth/momentum screeners, keeping it at 'mostly distinct; minor overlap risk' rather than the fully distinct anchor.

4 / 5

Total

16

/

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

skill_md_line_count

SKILL.md is long (672 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository

tradermonty/claude-trading-skills

Path

skills/canslim-screener/SKILL.md

Reviewed

Table of Contents

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