feature-discover

feature-discover is a command for coding agents from axiomantic/spellbook. It costs 27 tokens per session (5,742 once invoked), scanned A, original, MIT.

A command for investigating an unfamiliar or unclear feature request before design and implementation. It explores the codebase, identifies ambiguity, asks structured questions, and records what has been learned.

In plain words
What is it for?
Use it to research existing code, clarify feature requirements, create an understanding document, and obtain a critical review of the findings.
Why use it?
It prevents implementation from being based on guesses or incomplete requirements. The workflow only moves forward when the discovery checks are complete.

Command

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 commands/axiomantic/spellbook/feature-discover
Clone the repo
git clone --depth 1 https://github.com/axiomantic/spellbook

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for feature-discover

README.md
[![agentmods](https://agentmods.dev/badge/commands/axiomantic/spellbook/feature-discover.svg)](https://agentmods.dev/commands/axiomantic/spellbook/feature-discover)
Your own site
<a href="https://agentmods.dev/commands/axiomantic/spellbook/feature-discover"><img src="https://agentmods.dev/badge/commands/axiomantic/spellbook/feature-discover.svg" alt="Measured on agentmods" height="20"></a>
Per session 27 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 5,742 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 $0.00027 $0.05742
Opus 5 $0.00014 $0.02871
Sonnet 5 $0.00005 $0.01148
Haiku 4.5 $0.00003 $0.00574

Measured yesterday against content hash 06861eef8097, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

feature-discover 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.

commands/feature-discover.md · 651 lines

How it starts

The opening of the file, as written. The whole thing — 651 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Feature Discovery (Phase 1.5)

Before ANY Phase 1.5 work begins, verify:

# VERIFICATION TEMPLATE — not executable; substitute actual session values

Required: needs_research is true
  Current: [SESSION_PREFERENCES.need_flags.needs_research]
  → If not needs_research: STOP. This phase does not run.
     (needs_research gates BOTH Research (Phase 1) and Discovery (Phase 1.5);
      a single inclusive-OR flag — unfamiliar code OR fuzzy requirements.)

Required: Phase 1 research complete
  Verify: SESSION_CONTEXT.research_findings populated
  Verify: Research Quality Score = 100% (or user-bypassed)

Required: Research was done by subagent (not in main context)

If ANY check fails: STOP. Return to Phase 1.

Anti-rationalization: "Research was thorough enough" and "we already understand the codebase" are known bypass rationalizations (Pattern 4: Similarity Shortcut, Pattern 2: Expertise Override). Run the check. Trust the process.

Invariant Principles

  1. Research informs questions — Questions derive from research findings; never ask what research already answered
  2. 100% completeness required — Proceed to design only when all 13 validation functions pass; no exceptions without explicit bypass
  3. Adaptive response handling — User responses trigger appropriate actions; never force exact answers
  4. Understanding document is the gate — Devil's advocate reviews the understanding document; approval unlocks design

Adaptive Response Handler (ARH) Pattern

Read the full file on GitHub · 651 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 · 651 lines · 27 tokens per session scan A 06861eef8097

Subscribe to this mod's changes

feature-discover is a command published in the GitHub repository axiomantic/spellbook (10 stars, last pushed yesterday), licensed MIT. It adds 27 tokens to every session and 5,742 once invoked, about $0.0001 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 2026-09-03.