feature-recon

feature-recon is a skill for Claude Code from iSerter/claude-feature-recon. It costs 100 tokens per session (2,468 once invoked), scanned A, original, MIT.

A codebase review that examines a project feature by feature and records its condition, evidence, and missing parts in reports and an HTML dashboard.

In plain words
What is it for?
Use it to review feature maturity, bugs, gaps, opportunities, and test coverage across a project.
Why use it?
It shows what the project actually contains and supports each finding with file-and-line references, while also stating what was not checked.

Skill for Claude Code

Written for Claude Code: ${CLAUDE_PLUGIN_ROOT} variable. Also seen: mentions CLAUDE.md; mentions subagents; mentions AGENTS.md.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the feature-recon plugin — 5 skills, 5 commands, 5 agents shipped together

Good fit Use it to review feature maturity, bugs, gaps, opportunities, and test coverage across a project.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add iSerter/claude-feature-recon
Claude Code
/plugin install feature-recon

Made for: Claude Code.

Or install feature-recon, the plugin that ships this one along with the rest of its 5 skills, 5 commands, 5 agents.

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-recon

README.md
[![agentmods](https://agentmods.dev/badge/skills/iserter/claude-feature-recon/feature-recon/github.svg)](https://agentmods.dev/skills/iserter/claude-feature-recon/feature-recon)
Your own site
<a href="https://agentmods.dev/skills/iserter/claude-feature-recon/feature-recon"><img src="https://agentmods.dev/badge/skills/iserter/claude-feature-recon/feature-recon/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for feature-recon

Your own site · 80×15
<a href="https://agentmods.dev/skills/iserter/claude-feature-recon/feature-recon"><img src="https://agentmods.dev/badge/skills/iserter/claude-feature-recon/feature-recon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,468 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00100 $0.02468
Opus 5 $0.00050 $0.01234
Sonnet 5 $0.00020 $0.00494
Haiku 4.5 $0.00010 $0.00247

Measured 9d ago against content hash 3cab6f8b1965, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

feature-recon 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 9d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (build_report.js, build_report.py, build_report.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/feature-recon/SKILL.md · 185 lines

How it starts

The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Feature Recon

A reconnaissance sweep, not an authoritative audit: it reports what it actually read, cites path:line for every claim, and declares its blind spots. Output is one JSON state file per feature, a rollup, and a self-contained HTML dashboard built from them.

Bundled files live beside this SKILL.md (${CLAUDE_PLUGIN_ROOT}/skills/feature-recon/): reference/report-spec.md, reference/lens-security.md, reference/lens-ux.md, build_report.sh, template.html. The review agents live at ${CLAUDE_PLUGIN_ROOT}/agents/. Always pass absolute paths.

build_report.sh is the only entry point you call: it runs whichever of python3 / node the machine has. Never call build_report.py or build_report.js directly, and never assume a runtime — if the wrapper reports that neither is installed, say so and stop at step 6, where the JSON state files are already complete and useful on their own.

Procedure

1. Resolve arguments

  • <recon-dir> — default docs/recon, or --dir <path>.
  • Explicit feature list, if the user gave one → skip step 3.
  • --sequential → skip the fan-out in step 4 and sweep features one at a time in this context.
  • --lens <list> — which review lenses to run: product (the default, and what a plain run does), security, ux, or all. Comma-separated. Never add a lens the user did not ask for: each one multiplies the agent count by the feature count, and the specialists exist to be opted into.

2. Orient once

Gather this once and reuse it in every subagent prompt, so N agents don't each re-derive it:

git rev-parse --short HEAD && git log -1 --date=short --format=%ad

Plus, from a quick look at the repo root: project name, stack/framework, and where these live — entrypoints/routes, domain modules, models/migrations, UI pages, tests. Read the root README and CLAUDE.md/AGENTS.md if present.

Two more, because the sweep cannot find its best findings without them:

  • Shared abstractions — the base controller/job/service classes, the middleware stack, how auth and tenancy are enforced, the shared HTTP/AI/queue client. These are what make a sibling comparison possible ("this job skips the guard its four siblings apply") and what turn eight identical findings into one cross-cutting entry.
  • Test conventions — how tests are named, where they live, how they are run.

Read the full file on GitHub · 185 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. 9d ago First seen · 185 lines · 100 tokens per session scan A 3cab6f8b1965

Subscribe to this mod's changes

feature-recon is a skill published in the GitHub repository iSerter/claude-feature-recon (6 stars, last pushed 1mo ago), licensed MIT. It adds 100 tokens to every session and 2,468 once invoked, about $0.0005 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.