Agent skill

Competitive Positioning

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

Competitive positioning, strategic group mapping, differentiation analysis, competitive advantage assessment, market positioning map, value chain positioning, brand positioning, cost leadership vs…

Apache-2.0Auto-check passedMarketing & SEO

Install Competitive Positioning

skills CLI
$ npx skills add agentii-ai/agentii-investment-intelligence --skill competitive-positioning -a claude-code

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

GitHub CLI
$ gh skill install agentii-ai/agentii-investment-intelligence competitive-positioning --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/industry-analysis/skills/agentii/competitive-positioning .claude/skills/competitive-positioning && 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
competitive-positioning
GitHub stars
207
Token cost
~1.8k tokens
SKILL.md length
666 words
Files
5 (incl. references)
Skills in repo
79
Repo updated
First seen
Licence
Apache-2.0

At a glance

Competitive positioning, strategic group mapping, differentiation analysis, competitive advantage assessment, market positioning map, value chain positioning, brand positioning, cost leadership vs…

  • Works in 5 steps: Retrieval Scope → Retrieval Strategy → Temporal Scope → …
  • Tasks that involve Positioning and messaging
  • SKILL.md covers Triggers, Defaults, Methodology and Output File, plus 6 more sections
  • Reaches agentii.ai

What it does

Competitive Positioning is an agent skill from agentii-ai/agentii-investment-intelligence. Competitive positioning, strategic group mapping, differentiation analysis, competitive advantage assessment, market positioning map, value chain positioning, brand positioning, cost leadership vs differentiation, niche strategy analysis, disruptive positioning

Its SKILL.md is about 1.8k 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, covering Positioning and messaging. 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 Positioning and messaging

Example prompts

  • “/competitive-positioning”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Retrieval Scope
  2. Retrieval Strategy
  3. Temporal Scope
  4. Tool Allowlist
  5. Protocol

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

Competitive Positioning loads about 1.8k tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 71 tokens; SKILL.md has 666 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~71
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.8k

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). 666 words, ~1,849 tokens.

Download SKILL.mdSave it as .claude/skills/competitive-positioning/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
competitive-positioning
description
Competitive positioning, strategic group mapping, differentiation analysis, competitive advantage assessment, market positioning map, value chain positioning, brand positioning, cost leadership vs differentiation, niche strategy analysis, disruptive positioning
multi_ticker_semantics
target_with_required_peers
temporal_scope.default_quarters
4
temporal_scope.max_quarters
10
temporal_scope.description
Typical lookback: 4 quarters, max: 10
retrieval_scope
unstructured_document_search
min_tool_diversity
8

competitive-positioning

Triggers

  • Competitive positioning
  • strategic group mapping
  • differentiation analysis
  • competitive advantage assessment
  • market positioning map
  • value chain positioning
  • brand positioning
  • cost leadership vs differentiation
  • niche strategy analysis
  • disruptive positioning

Defaults

ParameterDefault ValueRationale
ticker(required)Stock symbol to analyze
lookback_quarters4Standard 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: 10. 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 index
  • get_company_financials — consolidated IS/BS/CF highlights
  • list_coverage — universe-level coverage discovery
  • get_company_profile — sector/industry classification + metadata
  • read_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 (namespace param; default us-gaap — use ifrs-full for foreign filers)
  • read_source_pages — Layer 3 deep read of selected pages with table markers
  • search_keyword_in_source — Layer 2.5b keyword page filter for large documents
5. Protocol
  1. Pre-flight (mandatory): get_company_fiscal_calendar/{ticker} then get_ticker_coverage/{ticker}; route on coverage.
  2. Layer 1 — discovery: search_documents / search_sec_filings to find candidate filings by ticker/form_type/date.
  3. Layer 2 — page map: read_source_outline/{ticker}/{citation_id} — every description is platform-generated (description_provenance says which kind), so never quote it as the filing's words; a platform_metadata_placeholder means the page was not labelled, which is a reason to read it, not to skip it. Escalate to read_source_deep_outline only when labels can't disambiguate.
  4. Layer 2.5 (optional): search_keyword_in_source to narrow documents >50 pages.
  5. Layer 3 — deep read: read_source_pages/{ticker}/{citation_id}?pages=page<N>,... for the 3–5 selected pages only.
  6. Multi-period (if applicable): search_cross_period after fiscal-calendar resolution.
  7. Output: write the deliverable per ## Output File, then append to agentii.md.

Output File

Write the final deliverable to _cross/{descriptive-slug}_{YYYY-MM-DD_HHMM}_competitive-positioning_{affix}.md or _sector/{sector_name}/{YYYY-MM-DD_HHMM}_competitive-positioning_{affix}.md .

Show full SKILL.md (301 more words)Show less

Output Structure

  1. Executive Summary (≤200 words) — headline conclusions for the analysis.
  2. Data Sources — filings + structured endpoints used, with {ticker} {citation_id} page<N> citations.
  3. Analysis — the core findings, tables, and commentary for this dimension.
  4. Key Metrics — the quantitative results with QoQ/YoY context where relevant.
  5. 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) per contracts/output-frontmatter-schema.md.
  • Snapshot synthesis: after writing the deliverable, update the two-tier snapshot and classify findings as [FACT]/[DEDUCTED]/[VIEW] — see contracts/snapshot-synthesis.md.
  • Session archival: record the run under sessions/{YYYY-MM-DD}/ and update sessions/INDEX.md per contracts/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

ErrorAction
Ticker not foundSuggest checking spelling or trying list_coverage
No data availableFlag in Coverage Gaps, proceed with available data
API key invalidDirect user to agentii.ai/api-keys
MCP server unreachableRetry once; if persistent, halt with AGENTII_MCP_UNREACHABLE

References

© 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/competitive-positioning 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

Competitive Positioning 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.

Competitive Positioning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Competitive Positioning this skillagentii-ai/agentii-investment-intelligence207—~1.8kAutomated safety check: PassApache-2.0
Marketing OsYuzzyuk/marketing-os538—~2.5kAutomated safety check: PassMIT
Revenue Centric Designheliocosta-dev/revenue-centric-design740—~1.6kAutomated safety check: PassCustom licence
Startup Positioningferdinandobons/startup-skill1.2k—~4.6kAutomated safety check: PassMIT
Stanley Druckenmiller Investmenttradermonty/claude-trading-skills3k1 repos~2kAutomated safety check: PassMIT
B2b Playbookweilun88313/B2B-Playbook203—~3.1kAutomated safety check: PassProprietary

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Categories

Questions about Competitive Positioning

What does Competitive Positioning do?

Competitive positioning, strategic group mapping, differentiation analysis, competitive advantage assessment, market positioning map, value chain positioning, brand positioning, cost leadership vs…. Competitive Positioning is an agent skill from agentii-ai/agentii-investment-intelligence.

When should I use Competitive Positioning?

Competitive Positioning fits situations like: tasks that involve Positioning and messaging.

How do I install Competitive Positioning in Claude Code?

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

How do I install Competitive Positioning in Codex?

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

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

What does Competitive Positioning need to run?

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

Does Competitive Positioning 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 Competitive Positioning 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 Competitive Positioning use?

Competitive Positioning 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 Competitive Positioning use?

About 1.8k tokens (SKILL.md is roughly 7.4k 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 953 tokens, read only when the agent opens those files.

What are the alternatives to Competitive Positioning?

Skills that share tags, products or a category with Competitive Positioning: Marketing Os (Yuzzyuk/marketing-os, 538 stars), Revenue Centric Design (heliocosta-dev/revenue-centric-design, 740 stars), Startup Positioning (ferdinandobons/startup-skill, 1.2k stars) and Stanley Druckenmiller Investment (tradermonty/claude-trading-skills, 3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Competitive Positioning?

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.