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 agentmods add commands/0xmariowu/agentlint/algit clone --depth 1 https://github.com/0xmariowu/AgentLintWrote 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/commands/0xmariowu/agentlint/al)<a href="https://agentmods.dev/commands/0xmariowu/agentlint/al"><img src="https://agentmods.dev/badge/commands/0xmariowu/agentlint/al.svg" alt="Measured on agentmods" 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 | $0.00030 | $0.06332 |
| Opus 5 | $0.00015 | $0.03166 |
| Sonnet 5 | $0.00006 | $0.01266 |
| Haiku 4.5 | $0.00003 | $0.00633 |
Grade A, and why
al 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 5d 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 — 607 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/al — AgentLint
Diagnose, plan, fix. One command. User presses Enter twice at most.
Flow
Step 1: Module Selection
AskUserQuestion with defaults pre-selected (user can press Enter to accept):
AgentLint — which checks to run?
Core (deterministic, no AI calls) — default ON:
☑ Findability — can AI find what it needs?
☑ Instruction Quality — are your rules well-written?
☑ Workability — can AI build and test?
☑ Continuity — can next session pick up?
☑ Safety — are secrets and CI locked down?
☑ Harness — are Claude Code hooks/permissions safe?
Extended (opt-in, runtime-dependent):
☐ Deep Analysis — find contradictions, dead weight, vague rules (uses AI)
☐ Session Analysis — discover issues from your Claude Code session logs
[Enter to run with defaults]
Default: all 6 core dimensions. Extended analyzers are optional and will
show as n/a in the output unless explicitly checked. User presses Enter →
runs immediately.
Record the normalized choices in shell variables for the config write in Step 2. Core is currently all-or-nothing and defaults on; Deep/Session are the only runtime-selectable modules.
RUN_CORE=true
RUN_DEEP=false # set true only if Deep Analysis was selected
RUN_SESSION=false # set true only if Session Analysis was selected
Step 2: Init (first run only)
If ${CLAUDE_PLUGIN_DATA}/config.json doesn't exist, ask with default:
Where are your projects? [~/Projects]: ↵
Press Enter → uses ~/Projects. Save to ${CLAUDE_PLUGIN_DATA}/config.json.
Never ask for the projects root again.
After Step 1, always persist the selected scan options back into the same config file. The scan and verify steps must read this file instead of relying on stale shell variables; otherwise the config is dead state and Deep/Session choices are ignored.
CONFIG_DIR="${CLAUDE_PLUGIN_DATA:-$HOME/.al}"
CONFIG_FILE="$CONFIG_DIR/config.json"
mkdir -p "$CONFIG_DIR"
if [ ! -f "$CONFIG_FILE" ]; then
PROJECTS_ROOT_INPUT="${PROJECTS_ROOT_INPUT:-$HOME/Projects}"
node -e '
const fs = require("fs");
const file = process.argv[1];
const projectsRoot = process.argv[2];
fs.writeFileSync(file, JSON.stringify({
projects_root: projectsRoot,
modules: { core: true, deep: false, session: false }
}, null, 2) + "\n");
' "$CONFIG_FILE" "$PROJECTS_ROOT_INPUT"
fi
CONFIG_TMP="$(mktemp "$CONFIG_DIR/config.XXXXXX")"
node -e '
const fs = require("fs");
const [file, out, core, deep, session] = process.argv.slice(1);
const cfg = JSON.parse(fs.readFileSync(file, "utf8"));
cfg.modules = {
...(cfg.modules || {}),
core: core === "true",
deep: deep === "true",
session: session === "true"
};
fs.writeFileSync(out, JSON.stringify(cfg, null, 2) + "\n");
' "$CONFIG_FILE" "$CONFIG_TMP" "$RUN_CORE" "$RUN_DEEP" "$RUN_SESSION"
mv "$CONFIG_TMP" "$CONFIG_FILE"
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.
- 5d ago First seen · 607 lines · 30 tokens per session scan A ba1b4a141561
al is a command published in the GitHub repository 0xmariowu/AgentLint (55 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 6,332 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 2026-08-30.
Other commands, from other repositories
brooks-health
Run a Brooks-Lint codebase health dashboard across all four dimensions.
brooks-audit
Run a Brooks-Lint architecture audit.
brooks-debt
Run a Brooks-Lint tech debt assessment.
plan
Turn an approved spec into an implementation plan an engineer with zero context could execute — with a quality controller that blocks placeholders and hollow tasks.
simplify
The over-engineering review: five tags (delete, stdlib, native, yagni, shrink), a mandatory replacement per finding, and a real null result when there is nothing to cut.
audit
Onboard an existing codebase: every domain's checks over the whole tree, then a triaged plan to bring it in line.