Agent skill

Leading Indicators

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

Leading economic indicators analysis, ISM PMI, yield curve, consumer sentiment UMCSI, jobless claims, building permits, economic turning point detection, recession signal analysis

Apache-2.0Auto-check passed

Install Leading Indicators

skills CLI
$ npx skills add agentii-ai/agentii-investment-intelligence --skill leading-indicators -a claude-code

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

GitHub CLI
$ gh skill install agentii-ai/agentii-investment-intelligence leading-indicators --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/macro-strategy/skills/agentii/leading-indicators .claude/skills/leading-indicators && 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
leading-indicators
GitHub stars
207
Token cost
~2.1k tokens
SKILL.md length
896 words
Files
5 (incl. references)
Skills in repo
79
Repo updated
First seen
Licence
Apache-2.0

At a glance

Leading economic indicators analysis, ISM PMI, yield curve, consumer sentiment UMCSI, jobless claims, building permits, economic turning point detection, recession signal analysis

  • Works in 4 steps: Leading indicators framework —… → Knowledge entries — query… → Historical analogues — query… → …
  • 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

Leading Indicators is an agent skill from agentii-ai/agentii-investment-intelligence. Leading economic indicators analysis, ISM PMI, yield curve, consumer sentiment UMCSI, jobless claims, building permits, economic turning point detection, recession signal analysis

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/credit-analysis-methodology.md`, `references/leading-indicators-framework.md` and `references/modes.md`).

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.

Example prompts

  • “/leading-indicators”

Workflow steps

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

  1. Leading indicators framework — references/leading-indicators-framework.md (bundled methodology)
  2. Knowledge entries — query search_knowledge_entries for supplementary L1 frameworks
  3. Historical analogues — query search_by_analogue(market_regime, event_type)
  4. Real-time data — FRED (real rates, yield curve, money supply, credit spreads), ISM PMI, UMCSI, jobless claims, building permits, commodity…

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

Leading Indicators loads about 2.1k tokens when it runs, and up to ~7.8k if it reads all its reference files. Until then it costs about 50 tokens; SKILL.md has 896 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.8k

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). 896 words, ~2,096 tokens.

Download SKILL.mdSave it as .claude/skills/leading-indicators/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
leading-indicators
description
Leading economic indicators analysis, ISM PMI, yield curve, consumer sentiment UMCSI, jobless claims, building permits, economic turning point detection, recession signal analysis
multi_ticker_semantics
single_target
temporal_scope.default_quarters
4
temporal_scope.max_quarters
12
temporal_scope.description
4 quarters default for leading-indicators analysis; up to 12 for regime context.
retrieval_scope
structured_only
min_tool_diversity
3
parameter_free
false

Methodology inspired by publicly taught trading frameworks; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
lookback_quarters4Standard window for leading-indicators
gdp_forecast_lag6 monthsS&P 500 leads GDP with maximum statistical significance at the 6-month horizon (10-year rolling correlation avg: 0.56, 1960–2020)
indicator_frequencyweeklyMoney-market and survey indicators are tracked weekly; GDP is quarterly
portfolio_biaslong / neutral / shortMacro view resolves to one of three biases governing portfolio construction

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. Leading indicators framework — references/leading-indicators-framework.md (bundled methodology)
  2. Knowledge entries — query search_knowledge_entries for supplementary L1 frameworks
  3. Historical analogues — query search_by_analogue(market_regime, event_type)
  4. Real-time data — FRED (real rates, yield curve, money supply, credit spreads), ISM PMI, UMCSI, jobless claims, building permits, commodity prices, DXY

Methodology

Retrieval Scope

structured_only

Retrieval Strategy

This skill follows Branch (d) Simple Lookup from contracts/retrieval.md: query knowledge entries for L1 macro and leading-indicator frameworks; query search_by_analogue for historical regime analogues resolved from the indicator panel. No unstructured document retrieval.

Temporal Scope

See frontmatter temporal_scope block.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

The Pro-Trader Systematic macroeconomic framework: predict GDP → predict stock-market returns. S&P 500 leads GDP by 6 months (10-year rolling correlation avg 0.56). Two analytical axes: Growth drives earnings (E); Liquidity drives price (P). Detailed indicator methodology, thresholds, and decision rules are in references/leading-indicators-framework.md.

  1. GDP Baseline: Quadrinomial method (S&P 500 quarterly returns 6-month lagged vs real GDP). Four outcomes: 0-0 (both down, 8.2%), 1-1 (both up, 60.9%), 0-1 (profit-taking, 25.6%), 1-0 (unpredictable, 5.3%). 10-year rolling correlation check. Apply to EuroStoxx 600 vs Eurozone GDP. Skip China Shenzhen (unreliable correlation ~0.05).

  2. Money Market Indicators (earliest and most reliable):

    • Real interest rates: Nominal rate − CPI. Classify accommodative (< 0.5%), neutral (0.5–2%), restrictive (> 2%). Direction: falling = bullish; rising = bearish.
    • Yield curve (2s10s): Normal/steep = expansionary. Flattening = transition. Inverted = recession (6–18 month lead). Steepening from inversion = recovery. Monitor TED spread (3m LIBOR vs 3m Treasury) for global dollar stress.
    • Credit spreads: Hierarchy AA (ICE BofA, FRED) → BBB → CCC (junk moves first). Widening = contractionary → sell. Tightening = expansionary → buy. CCC blowout 400+ bps with AA calm = stress concentration.
    • M2 Money Supply: Accessory only. Accelerating + falling real rates = confirm expansion. Decelerating + rising real rates = confirm contraction. Divergence = flag regime ambiguity.
  3. Survey Indicators:

    • ISM Manufacturing PMI: > 50 expansion, < 50 contraction. Prioritize New Orders sub-component. PMI < 45 = strong contraction.
    • UMCSI Consumer Sentiment: < 70 recession warning, > 90 confident. Sharp MoM drops > 5 points often precede equity corrections.
  4. Commodity Prices: Copper (pervasive industrial demand proxy — compare LME vs Shanghai). Brent crude (rising with copper = demand-driven, bullish; rising without copper = supply shock, bearish).

  5. Market & Forex: S&P 500 as ultimate daily leading indicator. DXY strengthening = tightening global conditions; weakening = loosening. Cross-reference DXY direction against credit spread direction.

  6. Coincident & Lagging Cross-Check: CPI, PPI, NFP (coincident); GDP, earnings, unemployment (lagging). Never trade on lagging indicators alone.

  7. International: European ESI, China PMI (Official vs Caixin — Caixin often leads), Japan Tankan + JGB, UK Gilts + PMI, Germany Bund + Ifo, Italy BTP-Bund spread. Apply local CPI for real rates.

  8. Dashboard & Bias Resolution: Score 11 indicator categories (high-weight: real rates, yield curve, credit spreads, ISM PMI, S&P 500). ≥ 60% expansionary → net long. ≥ 60% contractionary → net short. Mixed → neutral.

  9. Analogue Retrieval: Query search_by_analogue with market_regime and event_type matching current configuration. Cite via /v/.

  10. Regime Classification: Expansion / Contraction / Stagflation / Recovery with Bear/Base/Bull probability weights and transition catalysts.

Show full SKILL.md (310 more words)Show less

Output File

{ticker}/{YYYY-MM-DD_HHMM}_leading-indicators_{affix}.md

Output Structure

  1. Executive Summary — GDP forecast (6-month forward), portfolio bias (long / neutral / short), regime classification with probability weights, top 3 signals in 2–3 sentences
  2. GDP Baseline — quadrinomial quadrant assignment, rolling correlation trend (S&P 500 vs GDP, 6-month lag), international comparison (Eurozone, China)
  3. Money Market Indicators — real interest rates (current level + direction), yield curve 2s10s (shape + direction), credit spreads (AA / BBB / CCC spreads over 10Y, direction + magnitude), M2 money supply growth (trend)
  4. Survey Indicators — ISM Manufacturing PMI (headline + new orders), UMCSI consumer sentiment (headline + expectations)
  5. Commodity & Market Signals — copper, Brent crude, S&P 500 quarterly direction, DXY trend
  6. International Context — European ESI, China PMI (official vs Caixin), other major economy indicators
  7. Leading Indicator Dashboard — weighted scorecard table with expansionary/contractionary signal count
  8. Regime Classification — regime type (Expansion / Contraction / Stagflation / Recovery), probability weights (Bear / Base / Bull), transition catalysts
  9. Portfolio Bias Recommendation — net long / net short / neutral with supporting evidence
  10. Historical Analogues — matched cases from search_by_analogue with /v/ citations
  11. Risk Assessment & Caveats — Fed intervention risk, signal divergence flags, data limitations
  12. Coverage Gaps — indicators with stale / missing data; degraded-mode annotations

Error Handling

ErrorFallback
No L1 frameworks foundProceed with the standard 10-indicator panel described in Protocol; flag degraded
search_by_analogue emptyNote "no relevant historical analogues found" — do not fabricate
Real-time data unavailableUse last-known values with staleness flag; indicate date of last observation
Credit spread data missing for one tierUse available tiers (AA/BBB) and note the gap; CCC data is most volatile and optional
Yield curve data flat / 2Y missingUse 3m10y or Fed funds vs 10Y as alternative curve; note substitution
International indicator missingProceed with US-only dashboard; flag international gap

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/leading-indicators-framework.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 4 other files (references) in plugins/vertical-plugins/macro-strategy/skills/agentii/leading-indicators of agentii-ai/agentii-investment-intelligence.

  • SKILL.md
  • references/credit-analysis-methodology.md
  • references/leading-indicators-framework.md
  • references/modes.md
  • references/wsp-methodology.md

Open the folder on GitHubat commit 86980e1

Compare with similar skills

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Lead Generation Research GuideRightNow-AI/openfang18k—~1.8kAutomated safety check: PassApache-2.0

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Questions about Leading Indicators

What does Leading Indicators do?

Leading economic indicators analysis, ISM PMI, yield curve, consumer sentiment UMCSI, jobless claims, building permits, economic turning point detection, recession signal analysis. Leading Indicators is an agent skill from agentii-ai/agentii-investment-intelligence.

How do I install Leading Indicators in Claude Code?

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

How do I install Leading Indicators in Codex?

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

Can I use Leading Indicators 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 leading-indicators -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/leading-indicators, .gemini/skills/leading-indicators, .github/skills/leading-indicators and .opencode/skills/leading-indicators in your project.

What does Leading Indicators need to run?

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

Does Leading Indicators 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 Leading Indicators 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 Leading Indicators use?

Leading Indicators 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 Leading Indicators use?

About 2.1k tokens (SKILL.md is roughly 8.4k 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 5.7k tokens, read only when the agent opens those files.

What are the alternatives to Leading Indicators?

Skills that share tags, products or a category with Leading Indicators: Lead Magnets (sickn33/agentic-awesome-skills, 47k stars), Lead Magnets (coreyhaines31/marketingskills, 54k stars), Apify Lead Generation (sickn33/agentic-awesome-skills, 47k stars) and Yield Intelligence (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 Leading Indicators?

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.