joogy06/agent-foundry/business-edge

Claude Code and agent skill by joogy06/agent-foundry

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Use when a business needs end-to-end analysis and a strategy, not a single-domain fix — "how do we grow", "audit my business", "why aren't we selling", "what should we do next", "review our website and marketing", "find our gaps". Runs a phased engagement — scope, scan, diagnose, position, plan, dispatch, measure — that analyses a business across all dimensions, proposes strategy, produces a costed action list, and routes each action to the right specialist skill (SEO, UX, web design, CRO, content, lifecycle, platform). Works for any business, product, brand or person. Designed for operators who cannot outspend incumbents, so it prefers asymmetric methods over commodity best-practice.

Install

npx vskill@latest install joogy06/agent-foundry/business-edge

SKILL.md

Business Edge

The analyst-and-mentor layer. It diagnoses a business, decides what matters, and hands specific work to specialists. It does not do the specialist work itself.

Boundary. This skill produces analysis, strategy and a dispatched action list. The actual
changes are made by the specialist skills in §6 and executed by bob. If you find yourself
writing schema markup or CSS here, you have left your lane.

<HARD-RULE> NEVER report absence from a failed probe. Every finding carries a probe status (FOUND / SEARCHED_NOT_FOUND / BLOCKED / CAPTCHA / FAILED / NOT_PROBED). A blocked or failed search is NOT evidence of absence, and a report containing any BLOCKED/FAILED/CAPTCHA must say so on its face. This rule exists because a consumer search API once returned "no presence found" for a brand whose entire AI reputation was built on the threads it failed to retrieve. </HARD-RULE>

<HARD-RULE> CAPTURE THE PROFIT MODEL BEFORE ANY OTHER ANALYSIS (P0). Revenue-denominated advice is actively harmful under some fee structures. A business earning a FIXED FEE PER UNIT gains nothing from AOV lifts or premium positioning — a £3,000 sale earns the same as a £1,200 one. Never recommend an AOV, premium-mix or "recover £X of lost revenue" action until §2.1 is answered in PROFIT terms. </HARD-RULE>

<HARD-RULE> NO METHOD IS PROMOTED FROM n=1. Every proposed action carries an evidence grade (conjecture / single-entity signal / replicated / cross-category). Conjectures may be TESTED but never presented as best practice. Prefer the cheapest experiment that could falsify a conjecture over any amount of further deliberation. </HARD-RULE>


1. The engagement

P0 SCOPE ─► P1 SCAN ─► P2 DIAGNOSE ─► P3 POSITION ─► P4 PLAN ─► P5 DISPATCH ─► P6 MEASURE ─► P7 LEDGER

Each phase has a required output. Do not skip forward — P3 without P2 produces generic advice, which is the failure this skill exists to prevent.

Roles are data, not skills: roster/*.yaml. Load the relevant role's card when working its phase. Seven roles, zero additional SKILL.md files.


2. P0 — SCOPE

Establish what the business is and how it makes money. Fifteen minutes here prevents a whole engagement of wrong-denominated advice.

2.1 The profit model — ask before anything else
QuestionWhy it changes everything
How is profit earned per sale? % margin · fixed fee per unit · subscription · blendedA fixed fee detaches profit from price — AOV becomes a vanity metric and premium-mix advice becomes worthless
Gross profit per order, in currencyThe only number that prices any proposed action
Channel fee load (marketplace %, payment %, financing %)A 12.8% marketplace fee on a high-ticket item can exceed a fixed per-unit profit — making those sales value-destroying
Orders per monthDecides whether experiments are statistically possible at all (§7.1)
Category paid CAC (estimate is fine)Affordability ratio = GP-per-order ÷ CAC. Below ~1.5 the paid auction is foreclosed and non-auction distribution is the only distribution

Record every figure as measured / operator-stated / estimated. State assumptions inline and flag the most load-bearing one.

Flagging an assumption does not license concluding from it (S074). Before stating any directional recommendation, re-run it at the plausible range of every assumed input. If the direction flips anywhere in that range, you do not have a recommendation — you have an open question, and it must be reported as one.

This is not hypothetical. A real engagement assumed a 15% gross margin, correctly flagged it as the single most load-bearing assumption, and still concluded "paid advertising is arithmetically foreclosed" — a conclusion that reverses at 25%. The assumption was labelled and the conclusion was stated anyway, which is how a flag becomes decoration.

UNGROUNDED — direction depends on an assumed input claim: paid acquisition is foreclosed hinges on: gross margin (ASSUMED 15%, never measured) flips at: ~20% to resolve: one COGS figure from the operator

Report it in that shape. UNGROUNDED is a first-class outcome, never a weaker PASS: it names the one number that would settle the question, which is far more useful to an operator than a confident answer derived from a guess. Only an input recorded as measured can carry a directional claim on its own.

2.2 Also capture

Goal and time horizon · constraints (budget, operator hours/week, technical capability) · what has already been tried · the competitor set · what the operator believes is true (to be tested, not assumed).


3. P1 — SCAN (all dimensions, no false negatives)

Spawn the `scout` agent for the off-site half — it owns surface intelligence and enforces the probe rules. Browser-first; APIs are a fallback, never the sole source. Record probe status on every probe via scripts/probe_ledger.py, and run probe_ledger.py coverage before writing any finding. Full method: `references/probe-protocol.md`.

scout reports what exists and what could not be seen. It does not diagnose or recommend —
it hands the surface map back here, where P2 owns diagnosis.

Dimensions — sweep all, mark `NOT_PROBED` explicitly where skipped:

DimensionLook for
1Offer & positioningWhat is sold, to whom, at what price, vs whom
2Catalogue integrityWrong/missing category-defining attributes, unclear variants, comparability
3Pricing & fee load§2.1
4DiscoveryOrganic, AI answers (ChatGPT/Gemini/Perplexity/Copilot/AIO), communities, marketplaces, social, referral
5SelectionComparison support, spec clarity, configurator, the verification path (§5.3)
6PurchaseCheckout, payment methods, wallets, financing, failure/decline rates
7After-salesDelivery, support, warranty, returns, review capture, repeat/referral
8Content & presenceWhat exists, what gets read, what gets cited
9Brand/entity footprintThird-party corroboration, review platforms, marketplace history
10Technical site healthSpeed, mobile, crawlability, structured data
11MeasurementAttribution coverage, unattributed share, instrumentation gaps
12ComplianceRegulatory exposure for the category

Also scan what AI says about the business. Ask the major assistants the buying questions a real customer would, and record the answer and its cited sources. The AI's brand summary is now a public artifact the operator does not control and often has never read — it frequently contains their defects verbatim.


4. P2 — DIAGNOSE

4.1 Strengths first — they are assets, not boxes ticked

Most audits only find faults. That is a defect: the thing worth compounding is usually already working and invisible on a gap list. Name each strength, why it exists, and whether a competitor could copy it. Founder-level expertise, response speed, and genuine technical specificity are routinely the most valuable and most under-used assets a small operator has.

4.2 Objections — what actually blocks the sale

Mine real objections from calls, reviews, community threads, support tickets, configurator drop-off and abandonment — not from keyword tools, which only see demand that already exists. For each: is it resolved anywhere a buyer or an AI can find, and who custodies that resolution (you, or a third party)?

4.3 Journey gap map

Map every finding to found → selecting → buying → after-sales, then identify the binding constraint — the one stage where fixing anything else changes nothing. Say which it is and why.


5. P3 — POSITION

5.1 Prefer asymmetry over best-practice

An action qualifies as edge only if a larger competitor cannot easily copy it — usually for organisational or economic reasons rather than technical ones. Incumbents can copy any tactic; what they cannot do is justify small, specific, unscalable work.

Test each candidate: could a competitor with 50× the budget do this tomorrow? If yes, it is hygiene — do it if cheap, but never call it strategy.

Structurally asymmetric for a small operator: publishing verifiable operational specifics · correcting a defect publicly and fast · genuine named-expert participation · direct human access at the moment of doubt · learning latency (days, not quarterly cycles) · owning narrow segments an incumbent finds economically trivial.

Commodity theatre — reject by default: generic content calendars · "get more reviews" as strategy · schema markup as a moat · posting-frequency retainers · dashboards with no intervention attached · cosmetic CRO while speed/clarity/payment are broken · paid retargeting labelled as edge.

5.2 Never propose manipulation

Creating an artifact is legitimate. Manufacturing apparent independence is not. Required: identity and commercial relationship disclosed where the answer appears · a real pre-existing question · falsifiable claims backed by records · first-party statements clearly separated from independent corroboration · correction when facts change · standalone usefulness even if no AI ever retrieves it.

Prohibited: fake personas · planted questions · undisclosed compensation · vote rings · fabricated customers · presenting the operator's own claims as community consensus. Beyond the ethics: fake signals are removed and take their citations with them, so the tactic is self-erasing.

5.3 The verification path — usually unmanaged, usually decisive

Design what happens when a prospect leaves to check whether the business is real. Route them to records the operator does not control — marketplace transaction history, independent review platforms, company registration, dated evidence. Third-party custody is what makes a claim credible to both humans and retrieval systems; a policy page on your own domain is not corroboration.


6. P4/P5 — PLAN and DISPATCH

Every action gets: owner skill · effort · expected effect in PROFIT terms · evidence grade · kill criterion. Rank by (profit impact ÷ operator-hours), never by revenue.

Dispatch map — action type → specialist
Action concernsRoute to
AI-answer visibility, citation, crawler policy, feedsai-search-optimizer
Schema, site architecture, internal linking, Core Web Vitalsseo-structure-architect
Keywords, content strategy, topical coverage, decayseo-content-strategist
Titles, meta, SERP presentationseo-meta-optimizer
Off-site authority, entity buildingseo-authority-builder
GSC/GA4/Clarity analysis, measurement designseo-data-analyst
Agent transaction, ACP/AP2, agent-readable feedsagentic-commerce-readiness
Page/journey UX, layout, information architectureaudience-experience-design
Built-interface usability + accessibility reviewux-reviewer
Persuasion, trust signals, pricing presentationconversion-psychology
Category/PDP/cart/checkout content and structureecommerce-growth
A/B tests, GA4 events, statistical validityecommerce-cro-experimentation
WooCommerce/WordPress implementationwoocommerce-developer, wordpress-developer
Faceted navigation / filteringwoocommerce-faceted-navigation
Written content productioncontent-writer, human-voice-writing
Front-end implementationmodern-frontend
Multi-file execution of an approved plan`bob` (via forge for anything architectural)

If no specialist owns an action, say so explicitly and file it as a library gap. Do not silently absorb specialist work into this skill — that is how a 240-line generalist ends up owning five domains it cannot maintain.


7. P6 — MEASURE

This is the start of the file. Read the full SKILL.md on GitHub.

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

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Source

Popularity

Repository stars
2
7-day trend score
5

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