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 skills/pyros-projects/limitless/dojonpx skills add pyros-projects/limitless --skill dojogit clone --depth 1 https://github.com/pyros-projects/limitlessWhat 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 | $0.00102 | $0.01697 |
| Opus 5 | $0.00051 | $0.00848 |
| Sonnet 5 | $0.00020 | $0.00339 |
| Haiku 4.5 | $0.00010 | $0.00170 |
Grade A, and why
dojo 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dojo — Skills Earn Their Place
Overview
A skill is a claim about future agent behavior. Untested claims ship as liabilities: self-generated, unverified skills measurably make agents worse (SkillsBench −1.3pp), while the same authorship under scored rollouts and validation gates lifts frozen-model accuracy by ~20pp (SkillOpt). It was never agents-writing-skills that failed — it was writing without verification. The dojo is where the verification happens: no skill ships on vibes.
Dojo covers the full lifecycle: create, edit, test, evaluate, package. For small edits, run only the kata that the change touches (a description tweak needs kata 6–7, not a full baseline) — say which kata you're skipping and why.
The Measurability Rule
Automated optimization needs a scalar grader; most skill output quality has none. So dojo never pretend-measures quality. Pass criteria are:
- Observable process checks — y/n facts about what the agent did ("scoped search to a discovered venue before deep-reading: y/n")
- Exact-match trigger tests — which skill did the router pick
Holistic output quality stays human/agent judgment, applied openly as judgment. If a skill someday has a real scalar grader, point SkillOpt at it instead of the dojo.
Tiers — Match Rigor to Skill Type
Full TDD on a reference doc is theater. Classify at intake:
| Tier | What it is | Example | Rigor |
|---|---|---|---|
| Discipline | Rules agents rationalize around | verification gates, TDD | Full RED-GREEN-REFACTOR + adversarial pressure variants |
| Technique | Multi-step orchestration | hivemind, suno-pack | Baseline-fail test + skilled-walkthrough test |
| Reference | Facts, flags, recipes | searxng | Correctness review + trigger eval |
Every tier gets the trigger eval (kata 6). Only discipline skills get adversarial variants (time pressure, sunk cost, authority pressure).
The Seven Kata
- Intake. What skill, which tier, new or edit. Design the test
scenarios NOW — one per archetype the skill claims to handle, each
with pre-written y/n pass criteria. Persist the battery immediately
to
~/.limitless/dojo/<repo-slug>/<skill>/<skill>-scenarios.md— and during kata 2–6, append every subagent prompt VERBATIM as actually sent, plus each run's result line. Dojo artifacts are skill-owned runtime output: keep them under~/.limitless/dojo/by default so target repos do not fill with raw training debris. Curated docs/examples may be copied into the repo only when the user asks. Hold 1–2 scenarios back: never used during iteration, run once at the end (kata 5). If a transcript or session sparked the skill, mine it for scenario material. Seereferences/pressure-testing.mdfor scenario and criteria design.
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.
- 3d ago First seen · 139 lines · 102 tokens per session scan A b4be33385186
dojo is a skill published in the GitHub repository pyros-projects/limitless (9 stars, last pushed 20d ago), licensed MIT. It adds 102 tokens to every session and 1,697 once invoked, about $0.0005 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 skills, from other repositories
expansion-grant-guard
YAML-based delegation grant ledger — issues, validates, and tracks scoped permission grants for sub-agent expansions with token budgets and auto-expiry.
create-skill
Scaffolds and validates new superpowers skills. Use when creating a new skill for this repository.
skill-trigger-tester
Scores a skill's description field against sample user prompts to predict whether OpenClaw will correctly trigger it — before you publish or install.
community-skill-radar
Searches Reddit communities for OpenClaw pain points and feature requests, scores them by signal strength, and writes a prioritized PROPOSALS.md for you to review and act on.
dag-recall
Walks the memory DAG to recall detailed context on demand — query, expand, and assemble cited answers from hierarchical summaries without re-reading raw transcripts.
memory-dag-compactor
Builds hierarchical summary DAGs from MEMORY.md with depth-aware prompts — leaf summaries preserve detail, higher depths condense to durable arcs, preventing information loss during compaction.