diagnostics-intake

A diagnostics intake workflow that creates a structured file describing a founder's product, business, market, stage, channels, and audience. It can gather information from a website, founder answers, or both.

In plain words
What is it for?
Use it to collect public company details and founder-provided context before researching competitors, channels, or growth ideas.
Why use it?
It replaces a longer discovery interview with a short, consistent starting record for the rest of the growth-planning workflow.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/acogood/diffmode_free/diagnostics-intake
Any agent
npx skills add acogood/diffmode_free --skill diagnostics-intake
Clone the repo
git clone --depth 1 https://github.com/acogood/diffmode_free

Made for: Claude Code, Codex.

Per session 125 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,849 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00125 $0.02849
Opus 5 $0.00063 $0.01425
Sonnet 5 $0.00025 $0.00570
Haiku 4.5 $0.00013 $0.00285

Measured 2d ago against content hash 378cb13c416a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

diagnostics-intake scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugin/skills/diagnostics-intake/SKILL.md · 224 lines

How it starts

The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Diagnostics — Intake (fast founder-input)

You produce a single file: WS/01-diagnostics/founder-input.md, in the exact schema the rest of the pipeline reads (enrichment, the constraints generator's field parser, and synthesis all key off these sections and labels). This replaces the slow 20-minute diagnostic interview with a ~2-minute path: research what's public, ask only what isn't.

This is capture, not analysis — no strategy, no recommendations, no channel picks. That separation is load-bearing: later stages depend on raw, un-editorialized founder context.

Inputs & Output

The invoker provides (do not hardcode absolute paths):

  • MODE A — url (a website, e.g. https://theona.ai): research the site + company.
  • MODE B — answers: a block of founder answers to the minimal question set below (the orchestrator collects these in the main thread; you format them).
  • MODE A+B: both — research the URL AND fold in any founder answers the brief passed (answers always win over researched guesses).
  • OUTPUT: write to WS/01-diagnostics/founder-input.md (path supplied by invoker).

If neither url nor answers is present, write the schema with every must-ask field as a [NEEDS FOUNDER INPUT: …] placeholder and report it — do not invent a business.

Field provenance (what to research vs what to ask)

Field Provenance
Product description, what it does, who it's for RESEARCHABLE (homepage/about)
Business model + pricing (tiers, free trial) RESEARCHABLE (pricing page)
Target-audience hypothesis (segments, ICP) RESEARCHABLE (site copy) + confirm
Competitive alternatives (direct + indirect) RESEARCHABLE (web research)
Product complexity ("explains itself" vs "needs a demo") RESEARCHABLE + confirm
Stage + current metrics (visitors, signups, MRR, paying customers) MUST ASK
Current acquisition sources / what's working (Q8 demand-gen signal) MUST ASK
Demand-gen vs CRO split (traffic problem vs conversion problem) MUST ASK
Budget (monthly marketing $, paid-ads yes/no) MUST ASK
Unfair advantage / rare assets (technical skill, industry access, network, domain expertise, existing audience) MUST ASK
Hours per week for growth (time they can commit) MUST ASK
Goal + timeline (target metric, deadline) MUST ASK
Tactics ruled out + competitor-dignity constraints MUST ASK (optional)

Read the full file on GitHub · 224 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 224 lines · 125 tokens per session scan A 378cb13c416a

Subscribe to this mod's changes

diagnostics-intake is a skill published in the GitHub repository acogood/diffmode_free (160 stars, last pushed 22d ago), licensed Apache-2.0. It adds 125 tokens to every session and 2,849 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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