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/hhamja/claude-code-harness/loop-engineeringnpx skills add hhamja/claude-code-harness --skill loop-engineeringgit clone --depth 1 https://github.com/hhamja/claude-code-harnessWhat 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 | $0.00052 | $0.00860 |
| Opus 5 | $0.00026 | $0.00430 |
| Sonnet 5 | $0.00010 | $0.00172 |
| Haiku 4.5 | $0.00005 | $0.00086 |
Grade A, and why
loop-engineering 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.
How it starts
The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Loop Engineering
Design the system that prompts the agent instead of prompting the agent each turn. A loop = run → collect feedback → self-correct, repeated until a verifiable stop condition holds.
Three principles (non-negotiable)
- The maker must not grade its own work. Implementation belongs to the configured implementer (
implementer:in loop.config.md — the Codex CLI when available, else the main agent); grading belongs to theverifiersubagent: fresh context, read-only,rubric.mdas its only standard. It returns a report; the main agent applies it to the loop files. - "Done" is a claim, not a proof. Every stop condition must be machine-checkable: a command that exits 0, a file that exists, an output that matches. Subjective criteria are banned from rubric.md.
- Memory lives on disk, not in context. Everything the next session needs is in
.claude/loop/—state.mdis the entry point; a fresh session reads that directory and resumes where the loop stopped.
Structure
Plugin = immutable logic (installed once per machine). .claude/loop/ = mutable state (once per project, created by /loopy:loop-init): goal.md, rubric.md, state.md (summary rewritten each cycle, max 100 lines), memory.md, review.md (human review summary, every cycle), loop.config.md (the only stack-dependent file).
Cycle shape
Implement (fresh codex exec per cycle when implementer: codex, prompt rebuilt from disk state; never resumed) → verifier grades ONCE at cycle end (phase gate — never per file edit) → main agent updates rubric checkboxes, rewrites state.md, records memory, overwrites review.md. Safety rails always on: max_iterations cap and 3-consecutive-failure escalation. An unbounded "repeat until pass" loop is forbidden.
Decision gates
Before a side-effecting action, classify by reversibility × impact. Reversible/local (edits, tests, local commits, work-branch push) → act or delegate, never re-ask. Irreversible or high-impact (merge to a protected branch, release, publish, external send, cost, destructive delete) → stop at a human gate. decision_gate.sh blocks the irreversible class; auto_push.sh (Stop hook) auto-pushes the work branch. The test is "can this be undone?", not "may I ask?".
What ships with it
5 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.
- yesterday First seen · 43 lines · 52 tokens per session scan A 8da78eff826f
loop-engineering is a skill published in the GitHub repository hhamja/claude-code-harness (1 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 860 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.
Other skills, from other repositories
weekly-digests
Generate a serial week-by-week narrative digest of a project's full claude-mem timeline. Splits the timeline into per-ISO-week files, then runs one consecutive subagent per week — each receiving the prior week's carry-forward block — to produce one chapter per ISO week of data. Use when asked for "weekly digests"…
skill-creator
Create, install, or update skills in the workspace. Use when (1) installing a skill from a URL or remote source, (2) creating a new skill from scratch, (3) updating or restructuring existing skills. Always use this skill for any skill installation or creation task.
skill-template
Template for creating new Agent Skills for context engineering. Use this template when adding new skills to the collection.
geo-audit
Full website GEO+SEO audit with parallel subagent delegation. Orchestrates a comprehensive Generative Engine Optimization audit across AI citability, platform analysis, technical infrastructure, content quality, and schema markup. Produces a composite GEO Score (0-100) with prioritized action plan.
Agent Development
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development…
skill-creator
Scaffold a new yoyo skill when a human or community issue asks for one ("add a skill for X", "create a skill that does Y"). Generates correct frontmatter, validates, writes to disk.