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 Parad0x-Labs/openclaw-skills --skill liquefy_token_guardgit clone --depth 1 https://github.com/Parad0x-Labs/openclaw-skillsWrote 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/parad0x-labs/openclaw-skills/liquefy_token_guard)<a href="https://agentmods.dev/skills/parad0x-labs/openclaw-skills/liquefy_token_guard"><img src="https://agentmods.dev/badge/skills/parad0x-labs/openclaw-skills/liquefy_token_guard/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/parad0x-labs/openclaw-skills/liquefy_token_guard"><img src="https://agentmods.dev/badge/skills/parad0x-labs/openclaw-skills/liquefy_token_guard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00051 | $0.00369 |
| Opus 5.5 | $0.00020 | $0.00148 |
| Sonnet 5.5 | $0.00010 | $0.00074 |
| Haiku 4.5 | $0.00005 | $0.00037 |
Grade A, and why
liquefy-token-guard 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 2mo 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
Liquefy Token Guard
Finds where your agents are wasting tokens and gives concrete fixes. Does NOT
silently rewrite prompts or guess costs — every report declares its truth mode
(exact, estimated, manual, or unavailable).
build_capsule is deterministic. It keeps the sharp parts and measures the
reduction — no LLM summary, no invented numbers.
Commands
| Command | Description |
|---|---|
scan_now |
Scan trace directory, update token ledger |
audit_now |
Detect waste: duplicate prompts, oversized context, model overkill |
set_budget |
Write/update token and cost budgets |
build_capsule |
Build a compact context capsule (deterministic, measured reduction) |
prime_next_run |
Install capsule bootstrap for the next run |
status |
Combined scan + report + audit summary |
recommend |
Action-focused fixes from current waste findings |
daily_guard |
Full scan/audit cycle, compact summary |
Install
cp -r skills/liquefy_token_guard ~/.openclaw/skills/
Pairs with Liquefy Archive — Token Guard finds waste, Archive moves cold
artifacts into verified .null vaults.
Source
github.com/Parad0x-Labs/openclaw-skills/tree/main/skills/liquefy_token_guard
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.
- 2mo ago First seen · 43 lines · 51 tokens per session scan A 372cbd4756cd
liquefy-token-guard is a skill published in the GitHub repository Parad0x-Labs/openclaw-skills (23 stars, last pushed 17d ago), licensed MIT. It adds 51 tokens to every session and 369 once invoked, about $0.0002 per session on Opus 5.5. 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 skills, from other repositories
brain-save
Create and manage governed memories by capturing one fact, decision, pattern, or convention, or by retiring an outdated memory. This is side-effecting and never auto-fires. Use when preserving a durable fact without a full recompile or changing a memory's lifecycle. Trigger with "/brain-save".
brain
Search and analyze governed notes, decisions, runbooks, and conventions, returning a qmd:// citation for every supported claim. Use when checking what the brain has captured about architecture, infrastructure, decisions, or operating practices. Trigger with "/brain", "ask the brain", or "check my knowledge base".
webgl-holographic-foil
A self-contained WebGL2 hero: thin-film interference over a crushed-foil surface whose palette shifts with the viewing angle; move the cursor to tilt the film.
ligandmpnn
Inverse-fold a backbone with ligand, nucleic-acid, and metal context using LigandMPNN (Dauparas et al. 2023, github.com/dauparas/LigandMPNN). Reach for this skill to redesign the residues lining a binding pocket around a bound small molecule or cofactor, to design metal-coordinating sites where the geometry must be…
evo2
Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring…
bo-process-optimization
A method for examining a business workflow to find waiting, rework, handoff waste, and other delays, then proposing improvements and ways to measure them. It separates observed facts from possible explanations and suggestions.