Agent skill

Qualitative Filtering

by agentii-ai in agentii-ai/agentii-investment-intelligence

Qualitative stock analysis, management operating plan MOP assessment, key performance indicator KPI identification, catalyst identification and classification, earnings call transcript analysis…

Apache-2.0Auto-check passedBusiness, Finance & HR

Install Qualitative Filtering

skills CLI
$ npx skills add agentii-ai/agentii-investment-intelligence --skill qualitative-filtering -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install agentii-ai/agentii-investment-intelligence qualitative-filtering --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/vertical-plugins/idea-generation/skills/agentii/qualitative-filtering .claude/skills/qualitative-filtering && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
qualitative-filtering
GitHub stars
207
Token cost
~2.5k tokens
SKILL.md length
999 words
Files
3 (incl. references)
Skills in repo
79
Repo updated
First seen
Licence
Apache-2.0

At a glance

Qualitative stock analysis, management operating plan MOP assessment, key performance indicator KPI identification, catalyst identification and classification, earnings call transcript analysis…

  • Works in 4 steps: Qualitative methodology —… → Company disclosures — SEC filings… → Earnings transcripts —… → …
  • Tasks that involve Meeting notes and agendas
  • SKILL.md covers Defaults, Preflight, Data Source Priority and Methodology, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Qualitative Filtering is an agent skill from agentii-ai/agentii-investment-intelligence. Qualitative stock analysis, management operating plan MOP assessment, key performance indicator KPI identification, catalyst identification and classification, earnings call transcript analysis, qualitative evidence gathering for investment thesis

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/modes.md` and `references/qual-methodology.md`).

It sits in Business, Finance & HR, covering Meeting notes and agendas, OKRs and executive reporting and Stock and market analysis. The repository describes itself as: Claude-type skills for institutional equity research — 25 AI agent skills with SEC filings, XBRL financials, earnings calendars, DCF/comps/LBO models, and PPT generation. Powered… The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Meeting notes and agendas
  • Tasks that involve OKRs and executive reporting
  • Tasks that involve Stock and market analysis

Example prompts

  • “/qualitative-filtering”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Qualitative methodology — references/qual-methodology.md (bundled MOP-KPI-Catalyst framework)
  2. Company disclosures — SEC filings (Business Description, Risk Factors, MD&A) via agentii MCP
  3. Earnings transcripts — search_documents(ticker={T}, form_type="earnings_call_transcript") → read_source_outline → read_source_pages…
  4. Strategy and case knowledge — search_investment_strategies + search_investment_cases + search_by_analogue

What it can do on your machine

Read from SKILL.md and the folder at commit 86980e1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Qualitative Filtering loads about 2.5k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 67 tokens; SKILL.md has 999 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~67
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~10k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from agentii-ai/agentii-investment-intelligence at commit 86980e1, republished under its Apache-2.0 licence (© agentii-ai). 999 words, ~2,464 tokens.

Download SKILL.mdSave it as .claude/skills/qualitative-filtering/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
qualitative-filtering
description
Qualitative stock analysis, management operating plan MOP assessment, key performance indicator KPI identification, catalyst identification and classification, earnings call transcript analysis, qualitative evidence gathering for investment thesis
multi_ticker_semantics
single_target
temporal_scope.default_quarters
4
temporal_scope.max_quarters
12
temporal_scope.description
Catalyst identification requires forward visibility; 4 quarters default.
retrieval_scope
unstructured_document_search
layer_tags
L2, L3
min_tool_diversity
3
parameter_free
false

Methodology fused from professional trading and investment frameworks; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
catalyst_window_days20-60Trading horizon for active positions
kpi_trend_min_quarters8Minimum quarters of KPI history for trend analysis
mgmt_track_record_years3Management credibility requires 3+ years of guidance vs actuals
catalyst_min_impact5%Minimum expected price impact to justify catalyst-driven trade

Preflight

Run canonical pre-flight per contracts/preflight.md. Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md — carry the _run_id from your first tool result and name yourself (and your parent, if you were spawned).

Data Source Priority

  1. Qualitative methodology — references/qual-methodology.md (bundled MOP-KPI-Catalyst framework)
  2. Company disclosures — SEC filings (Business Description, Risk Factors, MD&A) via agentii MCP
  3. Earnings transcripts — search_documents(ticker={T}, form_type="earnings_call_transcript") → read_source_outline → read_source_pages (citation prefix ect<N>; pages carry section_type in session_title and guidance/forward_looking/analyst_questions in labels)
  4. Strategy and case knowledge — search_investment_strategies + search_investment_cases + search_by_analogue

Methodology

Retrieval Scope

unstructured_document_search (earnings call transcripts + SEC disclosures)

Retrieval Strategy

Ownership & insider signals: search_institutional_holdings (top-10 holders + whale portfolios, direction=accumulating|reducing|new|exited) and search_insider_trades (Form-4 transactions with SEC URLs) are available as signal inputs.

Three-layer protocol from contracts/retrieval.md: the qualitative framework is bundled in references/qual-methodology.md. Earnings transcripts via search_documents(form_type="earnings_call_transcript") → read_source_pages (Layer 1→3). Strategy frameworks and historical analogues via MCP knowledge tools. Detailed methodology and catalyst classification in references/qual-methodology.md.

Temporal Scope

See frontmatter temporal_scope block.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

This skill implements qualitative investment analysis through a three-stage framework: MOP → KPI → Results. Companies do not publish an "MOP" — the analyst infers it by identifying KPIs first, then reverse-engineering the strategic plan. The analyst verifies the chain is intact and credible. A broken chain is the highest-quality short signal.

Detailed methodology: management assessment framework (Alpha/Beta/Delta), board quality checklist, mosaic theory triangulation, catalyst taxonomy, sector-specific KPI templates, and the 20+ pattern red flag catalog are in references/qual-methodology.md.

Critical distinction: Good company ≠ good stock. For the 20-60 day horizon, catalysts are required.

Steps
  1. KPI Identification: Identify industry-specific KPIs per the reference templates (SaaS, retail, manufacturing, financial services, healthcare). Determine leading vs. lagging. Assess consistency (changing KPIs = red flag), auditability, relevance. Map trends over 8+ quarters. KPI divergence from sector norms often explains quantitative outlier signals.

  2. MOP Analysis (5-dimension scorecard, each 0-10, composite < 25 = high risk): Extract from earnings calls, presentations, MD&A. Infer the MOP: forward-looking statements → recurring themes → strategic narrative → test consistency/credibility. Score on: Track Record (30%, 3yr+ guidance vs. actuals), Consistency (20%), Realism (20%), Alignment (15%, insider ownership + compensation structure), Disclosure Quality (15%). Red flags: transformational M&A without plan, repeated guidance misses, high SBC with low hurdles, C-suite turnover within 18 months.

  3. Management Team (Alpha/Beta/Delta): Alpha (CEO) — track record, capital allocation, communication style. Primary Betas (CFO, CPO, CTO, Corp Dev, CMO) — depth and tenure. Red flags: cluster departures, CFO departure near guidance, cluster insider selling.

  4. Board Assessment: Independence ≥ 75%, expertise present, financial expert on audit committee. "Political incest" check: management/board overlap. Red flags: classified board, supermajority voting, tenure > 15yr, CEO as Chair, related-party transactions.

  5. Industry Analysis: Five Forces and SWOT as thinking prompts, not rigid boxes. Read competitor 10-Ks to cross-check management narrative. Do not trust management pronouncements on competition — verify independently. "Explain to 10-year-old" test: describe in 1-2 sentences. "3-5 factors" rule: identify drivers that matter; more than 5 = spread too thin. For formal peer-set construction and relative benchmarking, defer to the peer-bench skill rather than rebuilding it here.

  6. Consensus Reconstruction (run after steps 1-5, never before — reading consensus early anchors the analysis to the expectation it is meant to test): Establish the published sell-side average and its dispersion, then triangulate the effective buy-side expectation, which typically moves ahead of the published figure. Weight recent revisions over the stale average. Output a range with a direction, never a point estimate. Wide dispersion = no consensus exists, so the disconnect framing does not apply; tight dispersion with stale revisions is the highest-value setup. If the buy-side bar cannot be triangulated, mark the disconnect unquantified and flag a coverage gap rather than substituting the published number. State the variant view as: market expects X, evidence indicates Y, because [KPI/MOP finding], closing when [catalyst] by [date].

  7. Catalyst Identification: Identify all catalysts within 20-60 days. Classify: Earnings / Corporate Action / Regulatory / Management / Industry / Macro. Assess: specificity (dateable?), magnitude (≥ 15% high, 5-15% standard, < 5% insufficient), probability, binary vs. spectrum (binary → reduce size). Tumbleweed test: < 1 non-earnings press release/month = avoid. Catalyst stacking: multiple = higher conviction; zero = investment, not trade.

  8. Red Flag Scan: Scan against catalog (see reference). 3+ flags = hard stop for longs. Key flags: non-recurring charges in 3+ of 4 quarters, SBC > 10% revenue, GAAP losses + non-GAAP profits, trade data contradicts management, competitor filings describe different dynamics.

  9. MCP Integration: search_investment_strategies(kind=qualitative) → search_investment_cases(domain=catalyst_driven) → search_by_analogue(event_type, company_situation). Handoff: conviction score (1-10), quantified consensus disconnect (or unquantified), ranked catalyst calendar, KPI summary, MOP score, management/board flags, red flag count.

Show full SKILL.md (204 more words)Show less

Output File

{ticker}/{YYYY-MM-DD_HHMM}_qualitative-filtering_{affix}.md

Output Structure

  1. Executive Summary — Qualitative conviction level, key catalyst, MOP credibility rating
  2. KPI Analysis — Industry-specific KPIs, trend assessment, leading vs. lagging classification
  3. MOP Assessment — Management strategy, credibility evaluation, red flags, track record
  4. Business Quality — Competitive position, industry dynamics, product/service assessment
  5. Consensus Disconnect — Published sell-side average and dispersion, triangulated buy-side range with direction, the quantified gap (or unquantified), and the variant view in one structured sentence
  6. Catalyst Calendar — All identified catalysts with type, date, expected impact, probability
  7. Earnings Call Analysis — Key takeaways from recent transcripts, management tone, analyst sentiment
  8. Qualitative Red Flags — Governance concerns, strategy pivots, disclosure quality issues
  9. Knowledge Integration — Matched strategies and historical analogues with /v/ citations
  10. Handoff Summary — Conviction score, priority catalyst, recommended next step (template/proceed/watch)
  11. Coverage Gaps — Data limitations, degraded-mode annotations

Error Handling

ErrorFallback
search_documents(form_type="earnings_call_transcript") returns emptyUse SEC filings only; flag transcript gap
No catalyst within 60-day windowFlag as watchlist item; do not force a catalyst
search_by_analogue returns emptyNote "no relevant analogues found"; do not fabricate

Memory Load

See contracts/memory-load.md.

Snapshot

See contracts/snapshot-synthesis.md.

Final Summary (TUI)

Include ### Key Citations block with 0-10 clickable /v/ URLs.

References

  • references/qual-methodology.md
  • contracts/citation-and-memory.md
  • contracts/output-frontmatter-schema.md
  • contracts/memory-load.md
  • contracts/snapshot-synthesis.md
  • contracts/preflight.md
  • contracts/retrieval.md

© agentii-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in plugins/vertical-plugins/idea-generation/skills/agentii/qualitative-filtering of agentii-ai/agentii-investment-intelligence.

  • SKILL.md
  • references/modes.md
  • references/qual-methodology.md

Open the folder on GitHubat commit 86980e1

Compare with similar skills

Qualitative Filtering next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Qualitative Filtering compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Qualitative Filtering this skillagentii-ai/agentii-investment-intelligence207—~2.5kAutomated safety check: PassApache-2.0
Earnings AnalysisWind-Alice/AliceMarket1303 repos~2.2kAutomated safety check: PassNone
Equity Research Corebyteseek/Mira275—~1.9kAutomated safety check: PassApache-2.0
Earnings Recaphimself65/finance-skills3.4k—~1.7kAutomated safety check: PassMIT
Xvary Stock Researchsickn33/agentic-awesome-skills47k2 repos~952Automated safety check: PassMIT
Catalyst ConfirmationSuperior-Trade/superior-skills214—~667Automated safety check: PassMIT

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Questions about Qualitative Filtering

What does Qualitative Filtering do?

Qualitative stock analysis, management operating plan MOP assessment, key performance indicator KPI identification, catalyst identification and classification, earnings call transcript analysis…. Qualitative Filtering is an agent skill from agentii-ai/agentii-investment-intelligence.

When should I use Qualitative Filtering?

Qualitative Filtering fits situations like: tasks that involve Meeting notes and agendas; tasks that involve OKRs and executive reporting; tasks that involve Stock and market analysis.

How do I install Qualitative Filtering in Claude Code?

Run `npx skills add agentii-ai/agentii-investment-intelligence --skill qualitative-filtering -a claude-code`. Or copy the skill folder (plugins/vertical-plugins/idea-generation/skills/agentii/qualitative-filtering in agentii-ai/agentii-investment-intelligence) into .claude/skills/qualitative-filtering in your project. Claude Code loads it when a task matches its description.

How do I install Qualitative Filtering in Codex?

Run `npx skills add agentii-ai/agentii-investment-intelligence --skill qualitative-filtering -a codex`. Or copy the skill folder (plugins/vertical-plugins/idea-generation/skills/agentii/qualitative-filtering in agentii-ai/agentii-investment-intelligence) into .agents/skills/qualitative-filtering in your project. Codex loads it when a task matches its description.

Can I use Qualitative Filtering in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agentii-ai/agentii-investment-intelligence --skill qualitative-filtering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qualitative-filtering, .gemini/skills/qualitative-filtering, .github/skills/qualitative-filtering and .opencode/skills/qualitative-filtering in your project.

What does Qualitative Filtering need to run?

SKILL.md names no scripts, command-line tools or credentials: Qualitative Filtering is instructions for the agent only.

Does Qualitative Filtering access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Qualitative Filtering safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Qualitative Filtering use?

Qualitative Filtering is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qualitative Filtering use?

About 2.5k tokens (SKILL.md is roughly 9.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.9k tokens, read only when the agent opens those files.

What are the alternatives to Qualitative Filtering?

Skills that share tags, products or a category with Qualitative Filtering: Earnings Analysis (Wind-Alice/AliceMarket, 130 stars), Equity Research Core (byteseek/Mira, 275 stars), Earnings Recap (himself65/finance-skills, 3.4k stars) and Xvary Stock Research (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qualitative Filtering?

agentii-ai (a GitHub user) maintains it in agentii-ai/agentii-investment-intelligence, which has 207 GitHub stars. The repository holds 79 skills in this directory. The repository was last updated on September 29, 2026.

Source: agentii-ai/agentii-investment-intelligence on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.