classify

A workflow for judging whether product features are genuinely distinctive or are standard implementation work, then ranking their contribution to the product’s advantage.

In plain words
What is it for?
Use it to classify features, assign innovation weights, regenerate a ranked feature index, and check whether the overall feature portfolio has a clear edge.
Why use it?
It helps separate features that make users switch from features competitors are expected to have, while flagging features that lack a stated link to the product’s unique value.

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/inscico/i-framework/classify
Any agent
npx skills add InSciCo/i-framework --skill classify
Clone the repo
git clone --depth 1 https://github.com/InSciCo/i-framework

Made for: Claude Code, Codex.

Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 899 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 $0.00062 $0.00899
Opus 5 $0.00031 $0.00449
Sonnet 5 $0.00012 $0.00180
Haiku 4.5 $0.00006 $0.00090

Measured yesterday against content hash 3e4c9d1623e2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

classify 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 yesterday.

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.

.claude/skills/classify/SKILL.md · 52 lines

How it starts

The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/classify — rank features innovation vs. implementation

You judge each feature against the UVP and assign a binary bucket plus a 0–100 weight, then produce the ranked "where's our edge" index and flag portfolio problems.

Reference: the rubric

A feature is innovation if it embodies the unfair advantage — a reason a user switches. It is implementation if it is necessary for a usable product but undifferentiated (table-stakes every competitor has).

  • Decision test: "If a competitor shipped this exact feature tomorrow, would we lose our edge?" Yes → innovation. "Would every competitor have this too?" Yes → implementation.
  • Weight: implementation → 0. Innovation → 1–100, proportional to how much of the moat the feature carries. Reserve 80–100 for the few features that are the reason to exist.
  • Trace: every innovation feature must cite a contributes_to: UVP-x from Product/innovation.md. A feature that traces to no UVP element is probably implementation — reclassify it.

Procedure

  1. Load the reference. Read Product/innovation.md for the enumerated UVP elements (UVP-1, UVP-2…). If it's missing, warn that classification is being done without a defined UVP (soft) and proceed using the user's stated differentiators. Also read Product/competition.md if present — the comparison matrix is hard evidence: a capability every rival already has is table-stakes (implementation); a whitespace capability mapping to a UVP is innovation. Prefer matrix evidence over gut.

  2. Target. Default to all Product/Features/F*.md; or a single feature if the user names one.

  3. Classify each. Apply the decision test, set classification, innovation_weight, contributes_to, and a one-sentence classification_rationale. Update the file frontmatter in place (don't touch the body).

  4. Portfolio sanity checks — report, don't silently pass. Use concrete thresholds so the check is consistent, not a vibe:

    • No innovation features (0%) → red flag: "nothing here is a reason to exist." Push the user to find the edge or revisit /innovate.
    • Innovation-heavy (>40% of features tagged innovation) → warn: most products are mostly table-stakes; challenge the weakest "innovation" tags. (>60% is a hard flag.)
    • Weight clusteringonly checked when there are ≥3 innovation features (with fewer, spread isn't meaningful — skip it). When it applies: if innovation weights span <30 points (e.g. all in 80–100), the weights aren't ranking anything; push for spread.
    • Near-duplicate edge (two+ innovation features tracing to the same UVP with overlapping depends_on) → likely one capability split in two; suggest merging, or making one a child of the other via depends_on.
    • Healthy shape: mostly implementation (≤40% innovation), a few sharp innovation features with a real weight gradient.

Read the full file on GitHub · 52 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. yesterday First seen · 52 lines · 62 tokens per session scan A 3e4c9d1623e2

Subscribe to this mod's changes

classify is a skill published in the GitHub repository InSciCo/i-framework (4 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 899 once invoked, about $0.0003 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 2026-08-31.