Geo Fundamentals
wasp-lang/wasp
Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).
Peer benchmarking, multi-ticker financial comparison, growth value matrix, composite z-score ranking, industry peer comparison, competitive benchmarking, sector relative performance, peer group…
Install Peer Bench
$ npx skills add agentii-ai/agentii-investment-intelligence --skill peer-bench -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agentii-ai/agentii-investment-intelligence peer-bench --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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/industry-analysis/skills/agentii/peer-bench .claude/skills/peer-bench && rm -rf skills-srcUse ~/.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/
Install the "peer-bench" agent skill from https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench into .claude/skills/peer-bench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "peer-bench", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/industry-analysis/skills/agentii/peer-benchType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add agentii-ai/agentii-investment-intelligence --skill peer-bench -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agentii-ai/agentii-investment-intelligence peer-bench --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench .agents/skills/peer-bench && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "peer-bench" agent skill from https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench into .agents/skills/peer-bench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "peer-bench", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agentii-ai/agentii-investment-intelligence --skill peer-bench -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agentii-ai/agentii-investment-intelligence peer-bench --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench .cursor/skills/peer-bench && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "peer-bench" agent skill from https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench into .cursor/skills/peer-bench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "peer-bench", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/agentii-ai/agentii-investment-intelligence.git --path plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add agentii-ai/agentii-investment-intelligence --skill peer-bench -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agentii-ai/agentii-investment-intelligence peer-bench --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench .gemini/skills/peer-bench && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "peer-bench" agent skill from https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench into .gemini/skills/peer-bench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "peer-bench", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install agentii-ai/agentii-investment-intelligence peer-benchInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add agentii-ai/agentii-investment-intelligence --skill peer-bench -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench .github/skills/peer-bench && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "peer-bench" agent skill from https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench into .github/skills/peer-bench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "peer-bench", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agentii-ai/agentii-investment-intelligence --skill peer-bench -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agentii-ai/agentii-investment-intelligence peer-bench --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench .opencode/skills/peer-bench && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "peer-bench" agent skill from https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench into .opencode/skills/peer-bench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "peer-bench", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
peer-bench- GitHub stars
- 207
- Token cost
- ~1.9k tokens
- SKILL.md length
- 693 words
- Files
- 5 (incl. references)
- Skills in repo
- 79
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Peer benchmarking, multi-ticker financial comparison, growth value matrix, composite z-score ranking, industry peer comparison, competitive benchmarking, sector relative performance, peer group…
- Works in 5 steps: Retrieval Scope → Retrieval Strategy → Temporal Scope → …
- Marketing & SEO work in your project
- SKILL.md covers Triggers, Defaults, Methodology and Output File, plus 6 more sections
- Reaches agentii.ai
What it does
Peer Bench is an agent skill from agentii-ai/agentii-investment-intelligence. Peer benchmarking, multi-ticker financial comparison, growth value matrix, composite z-score ranking, industry peer comparison, competitive benchmarking, sector relative performance, peer group analysis, industry leader comparison, financial ratio benchmarking
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/knowledge-frameworks.md`, `references/methodology.md` and `references/modes.md`).
It sits in Marketing & SEO. 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
- Marketing & SEO work in your project
Example prompts
- “/peer-bench”
Workflow steps
5 steps, taken from the step headings in SKILL.md.
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
Hosts in commands or code, which the agent is likely to contact:
agentii.ai
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
Peer Bench loads about 1.9k tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 68 tokens; SKILL.md has 693 words of instructions outside code blocks.
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). 693 words, ~1,910 tokens.
Download SKILL.mdSave it as
.claude/skills/peer-bench/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- peer-bench
- description
- Peer benchmarking, multi-ticker financial comparison, growth value matrix, composite z-score ranking, industry peer comparison, competitive benchmarking, sector relative performance, peer group analysis, industry leader comparison, financial ratio benchmarking
- multi_ticker_semantics
- target_with_required_peers
- temporal_scope.default_quarters
- 4
- temporal_scope.max_quarters
- 12
- temporal_scope.description
- Typical lookback: 4 quarters, max: 12
- retrieval_scope
- unstructured_document_search
- min_tool_diversity
- 9
peer-bench
Triggers
- Peer benchmarking
- multi-ticker financial comparison
- growth value matrix
- composite z-score ranking
- industry peer comparison
- competitive benchmarking
- sector relative performance
- peer group analysis
- industry leader comparison
- financial ratio benchmarking
Defaults
| Parameter | Default Value | Rationale |
|---|---|---|
| ticker | (required) | Stock symbol to analyze |
| lookback_quarters | 4 | Standard lookback for this skill type |
Methodology
1. Retrieval Scope
This skill operates with retrieval_scope: unstructured_document_search. It performs unstructured document search at scale via the three-layer retrieval protocol (Layer 1→2→2.5→3), escalating to read_source_deep_outline only when lightweight labels cannot disambiguate pages, plus structured XBRL where needed.
2. Retrieval Strategy
Follows the retrieval strategy decision tree in contracts/retrieval.md. Primary branch: (b)/(c) Unstructured Query via the three-layer protocol. Resolve the canonical ticker first (exact → fuzzy alias → share-class) before any data call.
3. Temporal Scope
Default lookback: 4 fiscal quarter(s); maximum: 12. The default balances recency against the trend window this analysis requires.
4. Tool Allowlist
Per frontmatter allowed_tools:
search_companies— ticker resolution + company context (entity-alias fuzzy match)search_xbrl_facts— primary structured financial facts (is_primary default)search_documents— Layer 1 document discovery (page-level silver records)search_sec_filings— Layer 1 SEC filing metadata indexget_company_financials— consolidated IS/BS/CF highlightsbatch_search— consolidate 3+ same-tool queries into one metered calllist_coverage— universe-level coverage discoveryread_source_outline— Layer 2 lightweight page map (description + keywords)read_source_deep_outline— Layer 2.5a deep page map (table_titles/drivers/metrics)list_xbrl_concepts— XBRL concept discovery for non-standard line items (namespaceparam; defaultus-gaap— useifrs-fullfor foreign filers)read_source_pages— Layer 3 deep read of selected pages with table markerssearch_keyword_in_source— Layer 2.5b keyword page filter for large documents
5. Protocol
- Pre-flight (mandatory):
get_company_fiscal_calendar/{ticker}thenget_ticker_coverage/{ticker}; route on coverage. - Layer 1 — discovery:
search_documents/search_sec_filingsto find candidate filings by ticker/form_type/date. - Layer 2 — page map:
read_source_outline/{ticker}/{citation_id}— everydescriptionis platform-generated (description_provenancesays which kind), so never quote it as the filing's words; aplatform_metadata_placeholdermeans the page was not labelled, which is a reason to read it, not to skip it. Escalate toread_source_deep_outlineonly when labels can't disambiguate. - Layer 2.5 (optional):
search_keyword_in_sourceto narrow documents >50 pages. - Layer 3 — deep read:
read_source_pages/{ticker}/{citation_id}?pages=page<N>,...for the 3–5 selected pages only. - Multi-period (if applicable):
search_cross_periodafter fiscal-calendar resolution. - Output: write the deliverable per
## Output File, then append toagentii.md.
Output File
Write the final deliverable to _cross/{descriptive-slug}_{YYYY-MM-DD_HHMM}_peer-bench_{affix}.md or _sector/{sector_name}/{YYYY-MM-DD_HHMM}_peer-bench_{affix}.md .
Show full SKILL.md (322 more words)Show less
Output Structure
- Executive Summary (≤200 words) — headline conclusions for the analysis.
- Data Sources — filings + structured endpoints used, with
{ticker} {citation_id} page<N>citations. - Analysis — the core findings, tables, and commentary for this dimension.
- Key Metrics — the quantitative results with QoQ/YoY context where relevant.
- Coverage Gaps & Citations — data not retrievable + citation index.
Citations & memory: follow contracts/citation-and-memory.md — ≥1 citation per 200 words; every material fact, table row, and metric is immediately followed by its inline clickable https://agentii.ai/v/{ticker}/{citation_id}/{N} link; a bottom Citations section provides a non-duplicative roll-up index; the closing TUI reply includes a compact Key Citations list (headline 5–10 facts) of clickable /v/ URLs; and append the run to agentii.md per contracts/agentii-md-schema.md.
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).
Memory & Snapshot
- Memory load (pre-flight): load prior workspace context for the ticker before retrieval — see
contracts/memory-load.md. - Structured output frontmatter: emit the FR-090 block (
key_metrics,conclusions,facts_count,deducted_count,views_count,citation_count) percontracts/output-frontmatter-schema.md. - Snapshot synthesis: after writing the deliverable, update the two-tier snapshot and classify findings as
[FACT]/[DEDUCTED]/[VIEW]— seecontracts/snapshot-synthesis.md. - Session archival: record the run under
sessions/{YYYY-MM-DD}/and updatesessions/INDEX.mdpercontracts/session-format.md.
Final Summary (TUI)
End the closing chat reply with a compact Key Citations list (headline 5–10 facts), each a clickable https://agentii.ai/v/{ticker}/{citation_id}/{N} link, so the user can cmd+click straight to the exact SEC page. See contracts/citation-and-memory.md.
Error Handling
| Error | Action |
|---|---|
| Ticker not found | Suggest checking spelling or trying list_coverage |
| No data available | Flag in Coverage Gaps, proceed with available data |
| API key invalid | Direct user to agentii.ai/api-keys |
| MCP server unreachable | Retry once; if persistent, halt with AGENTII_MCP_UNREACHABLE |
References
- Methodology:
references/methodology.md— tool fallbacks, retrieval strategy, analysis framework - Output Structure:
references/output-structure.md— detailed deliverable sections and ordering
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.
© 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/industry-analysis/skills/agentii/peer-bench of agentii-ai/agentii-investment-intelligence.
- SKILL.md
- references/knowledge-frameworks.md
- references/methodology.md
- references/modes.md
- references/output-structure.md
Open the folder on GitHubat commit 86980e1
Compare with similar skills
Peer Bench 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Peer Bench this skillagentii-ai/agentii-investment-intelligence | 207 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Geo Fundamentalswasp-lang/wasp | 19k | 9 repos | ~861 | Automated safety check: Pass | MIT | |
| Ab Testingcoreyhaines31/marketingskills | 54k | 3 repos | ~2.8k | Automated safety check: Pass | MIT | |
| Hreflang and International SEOAgriciDaniel/claude-seo | 18k | 5 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Referralscoreyhaines31/marketingskills | 54k | 2 repos | ~2.4k | Automated safety check: Pass | MIT | |
| SEO GeoReScienceLab/opc-skills | 1.8k | 4 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 |
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Questions about Peer Bench
What does Peer Bench do?
Peer benchmarking, multi-ticker financial comparison, growth value matrix, composite z-score ranking, industry peer comparison, competitive benchmarking, sector relative performance, peer group…. Peer Bench is an agent skill from agentii-ai/agentii-investment-intelligence.
When should I use Peer Bench?
Peer Bench fits situations like: marketing & SEO work in your project.
How do I install Peer Bench in Claude Code?
Run `npx skills add agentii-ai/agentii-investment-intelligence --skill peer-bench -a claude-code`. Or copy the skill folder (plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench in agentii-ai/agentii-investment-intelligence) into .claude/skills/peer-bench in your project. Claude Code loads it when a task matches its description.
How do I install Peer Bench in Codex?
Run `npx skills add agentii-ai/agentii-investment-intelligence --skill peer-bench -a codex`. Or copy the skill folder (plugins/vertical-plugins/industry-analysis/skills/agentii/peer-bench in agentii-ai/agentii-investment-intelligence) into .agents/skills/peer-bench in your project. Codex loads it when a task matches its description.
Can I use Peer Bench 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 peer-bench -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/peer-bench, .gemini/skills/peer-bench, .github/skills/peer-bench and .opencode/skills/peer-bench in your project.
What does Peer Bench need to run?
SKILL.md names no scripts, command-line tools or credentials: Peer Bench is instructions for the agent only.
Does Peer Bench access the network?
SKILL.md names 1 domain. In commands or code: agentii.ai; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is Peer Bench 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 Peer Bench use?
Peer Bench 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 Peer Bench use?
About 1.9k tokens (SKILL.md is roughly 7.6k 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 883 tokens, read only when the agent opens those files.
What are the alternatives to Peer Bench?
Skills that share tags, products or a category with Peer Bench: Geo Fundamentals (wasp-lang/wasp, 19k stars), Ab Testing (coreyhaines31/marketingskills, 54k stars), Hreflang and International SEO (AgriciDaniel/claude-seo, 18k stars) and Referrals (coreyhaines31/marketingskills, 54k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Peer Bench?
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.