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/floomhq/moto/target-loopnpx skills add floomhq/moto --skill target-loopgit clone --depth 1 https://github.com/floomhq/motoWhat 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 | $0.00074 | $0.00634 |
| Opus 5 | $0.00037 | $0.00317 |
| Sonnet 5 | $0.00015 | $0.00127 |
| Haiku 4.5 | $0.00007 | $0.00063 |
Grade A, and why
target-loop 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Target Loop Skill
Purpose
A structured iterative improvement loop that continues until a target artifact reaches a defined quality bar. Prevents premature stopping and systematic drift from the goal.
Step 1: Define the target
Before iterating, clearly define:
- What is the artifact? (code, copy, design, document)
- What is "done"? (specific quality criteria, not subjective)
- What is the completion signal? (user says "good", tests pass, score threshold, etc.)
- Maximum iterations? (default: 10)
Example completion criteria:
- "Copy: no filler words, every sentence earns its place, reads in under 60 seconds"
- "Code: all tests pass, no TypeScript errors, no console.logs"
- "Design: passes QA checklist, no overlapping elements, all text fits"
Step 2: Baseline assessment
Before iterating, assess the current state:
- What's working well? (preserve this)
- What's the biggest problem? (fix this first)
- Score: X/10 with specific reasoning
Step 3: Iterate
For each iteration:
ITERATION N/[MAX]
Current score: X/10
Biggest issue: [specific problem]
Fix applied: [what was changed]
New score: X/10
Remaining issues: [list]
Iteration priorities:
- Fix the thing that matters most first
- Don't fix multiple independent problems at once (hard to verify)
- After each fix, re-evaluate the full artifact - one fix often creates another problem
Step 4: Completion check
Before stopping, verify:
- Does the artifact meet ALL stated completion criteria?
- Is there anything that would make a skeptical reviewer reject this?
- Has the core goal been preserved through iterations?
If any check fails, continue iterating.
Step 5: Final output
When done:
TARGET LOOP COMPLETE
Iterations: N
Final score: X/10
What changed: [summary of improvements]
Result: [the final artifact]
Anti-patterns
- Stopping at 8/10: "Good enough" is not the goal. If the bar was set, hit it.
- Fixing symptoms: If the same type of issue keeps appearing, there's a root cause to fix.
- Losing the core: Iterations can drift from the original goal. Re-read the target criteria every 3 iterations.
- Infinite loops: Set a max iteration count. If not converging, the criteria may need refinement.
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 · 80 lines · 74 tokens per session scan A d7157fef91cc
target-loop is a skill published in the GitHub repository floomhq/moto (32 stars, last pushed 2mo ago), licensed MIT. It adds 74 tokens to every session and 634 once invoked, about $0.0004 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 2026-08-30.
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