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.
npx skills add FerroxLabs/ijfw --skill ijfw-summarizegit clone --depth 1 https://github.com/FerroxLabs/ijfwWrote 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/ferroxlabs/ijfw/ijfw-summarize)<a href="https://agentmods.dev/skills/ferroxlabs/ijfw/ijfw-summarize"><img src="https://agentmods.dev/badge/skills/ferroxlabs/ijfw/ijfw-summarize.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 5 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00032 | $0.00325 |
| Opus 5 | $0.00016 | $0.00162 |
| Sonnet 5 | $0.00006 | $0.00065 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
ijfw-summarize 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.
What it actually says
Scan the codebase and generate an optimized project context file.
Process
- Read: package.json, tsconfig.json, Cargo.toml, pyproject.toml, go.mod, Dockerfile, docker-compose.yml, .env.example, Makefile -- whatever exists.
- Scan: directory structure (2 levels deep), test framework, linter config, CI config.
- Detect: language, framework, database, ORM, auth approach, deployment target.
- Identify: key directories, entry points, API route patterns, shared utilities.
Output
Write a CLAUDE.md (or platform equivalent) with:
# Project Context
Stack: <framework> / <language> / <database>
Architecture: <pattern -- monolith, microservices, serverless, etc.>
Entry: <main entry point(s)>
Tests: <framework + command to run>
Lint: <tool + command>
## Structure
<key directories and their purpose, 1 line each>
## Patterns
<established code patterns to follow, 1 line each>
## Key Files
<important files a new contributor should know about>
Rules:
- Max 50 lines. This loads every session.
- No boilerplate explanations. Just facts.
- If uncertain about a pattern, omit it -- don't guess.
What ships with it
3 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.
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.
- 2d ago First seen · 46 lines · 32 tokens per session scan A bd737f053205
ijfw-summarize is a skill published in the GitHub repository FerroxLabs/ijfw (210 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 325 once invoked, about $0.0002 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 2026-09-05.
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