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.
/plugin marketplace add iSerter/claude-feature-recon/plugin install feature-reconWrote 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.
[](https://agentmods.dev/skills/iserter/claude-feature-recon/feature-recon)<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.
<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>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.
| Model | Per session | Once 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 | $0.00247 |
Grade A, and why
feature-recon scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Recon
A reconnaissance sweep, not an authoritative audit: it reports what it actually read, cites
path:line for every claim, and declares its blind spots. Output is one JSON state file per
feature, a rollup, and a self-contained HTML dashboard built from them.
Bundled files live beside this SKILL.md (${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>— defaultdocs/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, orall. 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.
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/lens-e2e.jpg 46 KB
- assets/lens-product.jpg 44 KB
- assets/lens-security.jpg 46 KB
- assets/lens-ux.jpg 44 KB
- build_report.js 31 KB runs code
- build_report.py 27 KB runs code
- build_report.sh 2.6 KB runs code
- reference/lens-security.md 10 KB
- reference/lens-ux.md 9.2 KB
- reference/report-spec.md 19 KB
- template.html 101 KB
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.
- 9d ago First seen · 185 lines · 100 tokens per session scan A 3cab6f8b1965
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.
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