Install
$ agentstack add skill-taxueseek-skill-optimizer-mimo-skill-creator ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
Skill Creator — MiMo Code
Create skills that extend MiMo Code with specialized workflows. Leverages task, bash, read, write, edit, skill tools natively.
Three Gates (Pre-flight)
All must pass before starting. If any is no → stop.
- MiMo Code can't already do this well? → Skill is overhead.
- User will use it 5+ times? → One-shot → direct prompt.
- Model has it built-in? → Skill adds complexity, not value.
Skill Anatomy
skill-name/
├── SKILL.md # Required — frontmatter + instructions
├── scripts/ # Optional — executable code
├── references/ # Optional — loaded on demand
└── assets/ # Optional — templates, icons, fonts
Frontmatter — only 3 fields are read by MiMo Code:
name: kebab-case # Required. Letters, digits, hyphens. Max 64 chars.
description: > # Required. Max 500 chars. Triggering conditions ONLY.
Use when [trigger], [trigger], or [symptom].
hidden: true # Optional. true = loaded but not in available_skills list.
Placement — MiMo Code discovers SKILL.md recursively from:
- Project:
.mimocode/skills/**,.claude/skills/**,.agents/skills/**,.codex/skills/** - Global:
~/.config/mimocode/skills/**,~/.claude/skills/**,~/.agents/skills/** - Config:
skills.pathsandskills.urlsinmimocode.json
The #1 Mistake: Description Trap
When description summarizes the workflow, the model follows the description and skips the body.
# ❌ BAD: Summarizes workflow → model takes shortcut
description: Use for TDD — write test first, watch it fail, write minimal code
# ✅ GOOD: Triggering conditions only → forces model to read the body
description: Use when implementing features or bugfixes, before writing code
Formula: [Action verb] + [value]. Use when [trigger 1], [trigger 2], ... Include 5+ triggers. Add exclusions. Be slightly "pushy".
Creation: 7 Steps
1. Classify Complexity
| Tier | SKILL.md | Dirs | |------|----------|------| | Simple | many mediocre
- ALWAYS/NEVER in caps → yellow flag → reframe with reasoning
- Context window is shared — every token must earn its place
- Cross-reference by name (
compose:tdd), not @link (force-loads, burns context) - Target: redundant content \n\n\n## Task\n"
})
Verify baseline differs. Do NOT tell subagent it's being tested.
### 7. Iterate
Improve → re-test → repeat until user satisfied or progress stalls.
## Evaluation Pipeline
### Architecture
MiMo Code subagents return results in response but do NOT reliably write to disk. **Controller persists all results.** Use `spawn` for parallel eval, `run` for blocking graders.
### Flow
**1. Test cases** — 10-20 prompts, 7:2:1 ratio (common/edge/anomalous):
```json
[{"id": 0, "prompt": "user task", "expectations": ["includes X"]}]
2. Paired subagents — batch 2-3 cases (4-6 concurrent):
task({ action: "spawn", agent: "general", description: "eval-0-with",
prompt: "\n\nTask: " })
task({ action: "spawn", agent: "general", description: "eval-0-without",
prompt: "Solve this task without any external instructions.\n\nTask: " })
3. Persist on notification — immediately write responses:
evals/iter-N/eval-/with_skill.md
evals/iter-N/eval-/without_skill.md
4. Grade — run blocking grader per case:
{"expectations": [{"text": "...", "with_skill": "pass", "without_skill": "fail", "evidence": "..."}]}
5. Aggregate via bash + Python → benchmark.md:
| Metric | with_skill | without_skill | Delta |
| Pass rate | 85% ± 5% | 35% ± 8% | +50% |
6. Analyze — non-discriminating (always pass), flaky (high variance), broken (always fail both).
Metrics
| Metric | Target | |--------|--------| | Routing accuracy | > 90% | | Output usability | > 80% | | Redundant tokens | && zip -r my-skill.skill my-skill/ \ -x "my-skill/evals/" -x "my-skill/iter-/" -x "__pycache__*"
## 5 Common Failures
| # | Failure | Fix |
|---|---------|-----|
| 1 | Description too vague | 5+ trigger phrases |
| 2 | SKILL.md dumping ground (800+ lines) | < 500 lines, split to references/ |
| 3 | ALWAYS/NEVER caps | Explain reasoning |
| 4 | No smoke test | Run Step 6 before shipping |
| 5 | Description summarizes workflow | Triggering conditions ONLY |
## Meta-Advice
1. **Description is everything.** Great skill + bad description = invisible.
2. **Explain why.** Model generalizes better with reasoning.
3. **Draft then look fresh.** First draft: too detailed or too vague.
4. **Generalize from feedback.** Encode the principle, not the fix.
5. **Context window is shared.** Every token earns its place.
6. **Test before deploying.** 15 min testing saves hours debugging.
## Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- **Author:** [taxueseek](https://github.com/taxueseek)
- **Source:** [taxueseek/skill-optimizer](https://github.com/taxueseek/skill-optimizer)
- **License:** MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.