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/aziontech/webkit/component-verifygit clone --depth 1 https://github.com/aziontech/webkitWhat 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 | $0.00030 | $0.00392 |
| Opus 5 | $0.00015 | $0.00196 |
| Sonnet 5 | $0.00006 | $0.00078 |
| Haiku 4.5 | $0.00003 | $0.00039 |
Grade A, and why
component-verify 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 yesterday.
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
You are running /component-verify <name>. You do not write anything. You re-execute the read-only phases of /component-create against an existing component.
User input: $ARGUMENTS
What to do
- Resolve
.specs/<name>.md. If absent → exit with the path it expected. - Run
spec-validatoron the current spec content. Surface any failures. - Recompute
sha256(body)and compare to frontmatterchecksum. Mismatch → surface and exit. - Resolve the existing component dir at
packages/webkit/src/components/webkit/<category>/<name>/. Missing → exit. - Spawn
echo-reporteragainst the disk files. Surface its verdict (parity / mismatch / degraded). - Spawn
validate-component(pnpm webkit:lint, type-check, type-coverage, dts build, storybook build). - Print a one-table summary: spec status, hook verdict, echo verdict, validation pass/fail.
Rules
- Read-only. This command writes nothing — no specs, no
.vue, no logs (other than a one-line summary in.claude/logs/<run-id>.jsonl). - No retries on failure. Surface and exit. The user fixes the divergence (in the spec or in the
.vue) and re-runs. - No sub-agent spawned that writes. Forbidden:
scaffolder,storybook-writer,code-connect-writer,spec-author,spec-validator(which writes the checksum — exception: run it in dry-mode where it returns the verdict without flippingstatus).
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.
- yesterday First seen · 25 lines · 30 tokens per session scan A c4f5a93a14b7
component-verify is a command published in the GitHub repository aziontech/webkit (2 stars, last pushed 3d ago), licensed MIT. It adds 30 tokens to every session and 392 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 2026-08-31.
Other commands, from other repositories
speckit.tasks
Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.
speckit.implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
diagnose
Run sivtr doctor to check the environment, then investigate any failing checks.
test-changed
Run the full quality gate on only the files changed since branching from main.
route
Route notes from inbox to appropriate vault destinations.
cleanup-transcript
Clean up a video/audio transcript (SRT or plaintext) - identifies structure, fixes transcription errors, asks clarifying questions iteratively.