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/rordi-ai/loopbreaker/implement-featurenpx skills add rordi-ai/loopbreaker --skill implement-featuregit clone --depth 1 https://github.com/rordi-ai/loopbreakerWrote 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/rordi-ai/loopbreaker/implement-feature)<a href="https://agentmods.dev/skills/rordi-ai/loopbreaker/implement-feature"><img src="https://agentmods.dev/badge/skills/rordi-ai/loopbreaker/implement-feature.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 | $0.00056 | $0.01172 |
| Opus 5 | $0.00028 | $0.00586 |
| Sonnet 5 | $0.00011 | $0.00234 |
| Haiku 4.5 | $0.00006 | $0.00117 |
Grade A, and why
implement-feature 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 4d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement Feature
Make the frozen behavior contract true. Implementation may choose the design inside that boundary; it may not silently change the boundary.
Preflight
- Bind the active issue first:
loopbreaker link ISSUE. The admission hook is keyed to that binding — without it the gate is inert and pre-admission edits are silently allowed. This is the single easiest way to appear gated while being ungated. - Call
delivery_readiness. Stop unless discovery is satisfied, shape is ready, planning is ready, andplanning_review.approvedplusimplementation.admittedare true. - Call
review_substratefor the issue and return the exact active gate when held. - Confirm the requested work maps to named behavior IDs and planning work units.
- Read repository instructions and the production construction path.
- Inspect current status and preserve unrelated worktree changes.
If the issue is held at discovery, stop and use $discovery-interview; the
premise is not yours to author.
If there is no imported contract or planning profile, stop and use $plan-feature.
If planning is structurally ready but not independently approved, use
$review-planning; never self-approve from this skill.
If the requested change would add acceptance requirements, report the scope change
instead of coding it as an incidental improvement.
Execution: harness first, then code
Evidence is executed, never asserted. You cannot record a verdict; you can only
run a registered harness and let its exit code decide. review_record_evidence
still exists for supporting observations, but evidence recorded that way is marked
not-executed and will not verify an enforced behavior.
For each behavior, in this order:
- Write its harness before the implementation. Drive the behavior at the tier
its contract names — a real CLI/HTTP/subprocess boundary for
wired, a real deployed target forlive. The repository may have no test tooling at all; if so, standing that tooling up is part of the work, not a reason to skip it. - Register it in
harnesses.jsonwithid,tier,runner,target, and aproveslist naming this behavior.runner: "script"executes the target directly, so any toolchain works —bun,pytest, a shell script. The registry is a reviewed file; that is what keepsprovefrom being arbitrary execution. - Bind it:
loopbreaker bind BEHAVIOR --harness ID. Both directions must agree — the behavior names the harness and the entry names the behavior back. - Prove it RED through the gate:
loopbreaker prove BEHAVIOR. It must fail before the code exists. A harness that is green before the work proves nothing.
What ships with it
1 file 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.
- 4d ago First seen · 100 lines · 56 tokens per session scan A f017746ddd15
implement-feature is a skill published in the GitHub repository rordi-ai/loopbreaker (3 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 1,172 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 2026-08-31.
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