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-computegit 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-compute)<a href="https://agentmods.dev/skills/ferroxlabs/ijfw/ijfw-compute"><img src="https://agentmods.dev/badge/skills/ferroxlabs/ijfw/ijfw-compute.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 | $0.00066 | $0.01103 |
| Opus 5 | $0.00033 | $0.00551 |
| Sonnet 5 | $0.00013 | $0.00221 |
| Haiku 4.5 | $0.00007 | $0.00110 |
Grade A, and why
ijfw-compute 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 3d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compute over read. When data is large or repetitive, run a sandboxed script and surface only the result. The four trees below decide which lever to pull.
Compute tree -- run a sandboxed script
Use when the input is bigger than the answer: log files, CSV/JSON dumps,
file-tree walks, repeated string transforms, aggregate stats, deduping.
Call ijfw_run compute:python "<script>" for pandas / numpy / stdlib parsing.
Call ijfw_run compute:js "<script>" for JSON shape-checks, regex sweeps,
quick numeric work. Sandbox is allowlist filesystem (cwd + project root) +
best-effort OS-level network deny; opt-in with IJFW_COMPUTE_NET=1 if the
script needs egress. Default timeout 30s, hard cap 300s via
IJFW_COMPUTE_TIMEOUT_MS. Output cap 100MB; overflow lands in the on-disk log.
Example: a 40MB nginx log. Instead of reading 200k lines into context, run
ijfw_run compute:python "import collections,sys;c=collections.Counter();[c.update([l.split()[8]]) for l in open('access.log')];print(c.most_common(10))"
and surface the top-10 status-code summary.
Read tree -- skip compute, just read
Use when the file is small (<2k lines), the task is a code edit or config tweak, or the agent needs to reason about structure rather than aggregate content. Direct Read is cheaper than spinning a subprocess; compute has fixed startup overhead.
Example: editing a single function in mcp-server/src/server.js. Read the
file, edit it, move on. No compute call needed.
Index tree -- write findings to FTS5 for later search
Use after a compute or research step produces a finding worth recalling
across sessions. Call ijfw_run index:source <kind> <body> to write into
the per-project FTS5 db at <project>/.ijfw/index/compute.db. Schema is
raw table (source_kind, source, session_id, project_root, body, ts).
Per-write PRAGMA quick_check guards integrity.
Citation provenance (C9.6): pass --source=<pointer> before the body to
attach an origin (file path / observation kind / skill name). Search hits
surface this pointer + the session_id so users can trace where each row
came from. Omitted -> source stays NULL.
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.
- 3d ago First seen · 88 lines · 66 tokens per session scan A f9ddd52b5abe
ijfw-compute is a skill published in the GitHub repository FerroxLabs/ijfw (210 stars, last pushed yesterday), licensed MIT. It adds 66 tokens to every session and 1,103 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 2026-09-05.
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