tessl-skill-review
$
npx mdskill add aAAaqwq/AGI-Super-Team/tessl-skill-reviewScore skills against Tessl to ensure quality and readiness.
- Measures skill quality using automated evaluation and best practices.
- Depends on Tessl CLI for automated scoring and review workflows.
- Decides readiness by analyzing structure, triggering, and instructions.
- Delivers structured feedback for improvement and revision comparison.
SKILL.md
.github/skills/tessl-skill-reviewView on GitHub ↗
--- name: tessl-skill-review description: Evaluate, score, and review an Agent Skill or SKILL.md using Tessl as the primary evaluator. Use when asked to measure skill quality, score a skill, review a skill against best practices, compare before/after skill revisions, or generate structured improvement feedback for a skill directory or SKILL.md file. --- # Tessl Skill Review Use Tessl as the default scoring engine for skill quality review. This skill is for **measuring quality**, not just giving vibes. Prefer Tessl-backed review first, then add your own judgment on top. ## Primary use cases Use this skill when asked to: - score a skill - evaluate a `SKILL.md` - review a skill against best practices - compare two versions of a skill - decide whether a skill is ready to publish - find weaknesses in skill triggering, structure, or instructions ## Core workflow ### 1) Identify the review target Accept either: - a skill directory containing `SKILL.md` - a direct path to `SKILL.md` - a repo path with one or more skills to audit If the request is ambiguous, clarify which skill or directory to score. ### 2) Prefer Tessl review first If Tessl CLI is available, start with: ```bash tessl skill review <path> ``` Useful examples: ```bash tessl skill review ~/.openclaw/skills/meta-cognition tessl skill review ./skills/work-to-skill tessl skill review ./skills/some-skill/SKILL.md ``` If the exact CLI surface has drifted, inspect: ```bash tessl --help tessl skill --help ``` If Tessl is not installed, either: 1. install it with: ```bash curl -fsSL https://get.tessl.io | sh ``` 2. or use the bundled helper: ```bash scripts/review.sh <path> ``` The helper script will detect whether Tessl exists, print the install command if missing, and run `tessl skill review <path>` when available. ### 3) Extract a structured scorecard From Tessl review output, capture at least: - overall score - strongest areas - weakest areas - trigger/description quality issues - instruction clarity issues - missing examples / weak workflow guidance - context bloat or redundancy risks - publish-readiness judgment If Tessl returns category scores, preserve them verbatim where possible. ## Manual fallback rubric If Tessl cannot be installed or executed, do a manual review using this scoring rubric. Score each dimension from **1-5**: - **Trigger clarity**: does the description clearly say what the skill does and when to use it? - **Workflow executability**: can another agent follow the steps without guessing? - **Context efficiency**: is the skill lean, or does it waste context? - **Reusability**: does it avoid hidden tribal knowledge and local-only assumptions? - **Safety**: does it properly constrain risky or irreversible actions? Convert to a 100-point score: ```text Total = (sum of 5 dimension scores / 25) * 100 ``` Verdict bands: - **90-100**: publish-ready - **75-89**: strong, but improve a few areas - **60-74**: useful, but needs substantial revision - **<60**: not ready Always state clearly whether the score came from **Tessl** or from the **manual fallback rubric**. ## Secondary workflow: scenario-based evaluation When the user wants deeper validation, go beyond `skill review` and run scenario evals. Use Tessl scenario tooling when the question is not just “is this well-written?” but “does this skill actually improve agent performance?” Preferred flow: ```bash tessl scenario generate <path> tessl scenario run <path-or-scenario> ``` Use scenario evals for: - regression checks after editing a skill - comparing two versions of a skill - checking whether extra context actually helps - judging real task success rather than surface quality only ## What to look for in your analysis After Tessl output, add your own judgment across these dimensions: ### 1. Trigger quality - Is the frontmatter description specific enough to trigger reliably? - Does it say both **what the skill does** and **when to use it**? - Is it too vague, too generic, or too narrow? ### 2. Workflow quality - Are the steps executable? - Does the skill guide the agent through decisions, not just dump information? - Are fragile steps sufficiently constrained? ### 3. Context efficiency - Is the body concise enough? - Does it duplicate obvious model knowledge? - Should detailed material move into references instead of bloating `SKILL.md`? ### 4. Reusability - Would another agent instance be able to use this without extra tribal knowledge? - Are assumptions, prerequisites, and inputs explicit? ### 5. Safety and overreach - Does the skill push the agent toward risky or irreversible actions without proper checks? - Are approval boundaries and destructive actions handled clearly? ## Output format Use this output shape unless the user asks for another format: ```markdown ## Tessl Skill Review - Target: - Tessl overall score: - Verdict: ready / close / needs work / not publishable yet ## Strengths - ... ## Weaknesses - ... ## High-impact fixes 1. ... 2. ... 3. ... ## Suggested rewrite areas - frontmatter: - workflow: - examples: - references/scripts: ## Final recommendation - ... ``` ## Publishing / PR use When reviewing skills for a PR or public registry submission: - use Tessl score as an input, not the only decision-maker - call out any mismatch between score and real-world usefulness - flag private-environment coupling, hardcoded paths, secret handling, or weak public readability ## Anti-patterns Do not: - give a score without actually running Tessl when Tessl is available - confuse “nice writing” with “effective agent behavior” - accept a high-level skill that has no actionable workflow - ignore bloated context just because the prose sounds polished - assume a skill is good only because it is long ## Quick command checklist ```bash # install if needed curl -fsSL https://get.tessl.io | sh # or use the bundled helper scripts/review.sh <path> # inspect CLI if unsure tessl --help tessl skill --help # basic quality review tessl skill review <path> # deeper evals when needed tessl scenario generate <path> tessl scenario run <path-or-scenario> ``` ## Trigger phrases - “测一下这个 skill 的评分” - “帮我评估这个 SKILL.md” - “这个 skill 质量怎么样” - “用 Tessl 跑一下 skill review” - “compare these two skill versions” - “is this skill publish-ready?” - “score this skill” - “review this skill against best practices”
More from aAAaqwq/AGI-Super-Team
- a-fund-monitor监控 A 股基金实时估值与盘后净值,自动判断交易日并生成提醒或分析。
- account-executive>
- add-leadAdd company/person/relationship to CRM
- adsComprehensive ad account analysis across all major platforms (Google, Meta
- ads-agentAI-агент для управления Facebook рекламой. Вызывай для анализа, оптимизации, создания кампаний и отчётов.
- afrexai-compliance-auditRun internal compliance audits against major governance and security
- afrexai-personal-financeComplete personal finance system — budgeting, debt payoff, investing, tax optimization, net worth tracking, and financial independence planning. Use when managing money, building wealth, paying off debt, planning retirement, or optimizing taxes. Zero dependencies.
- after-salesUse when managing post-purchase experience, building customer loyalty, or increasing repeat purchases
- agent-contactsAI agent contacts — add, list, remove MCP contacts. Use when someone gives an agent URL, or when you need to view/remove contacts.
- agent-model-switcher批量查看和切换子 agent 的模型配置,用于统一调整多 agent 的 provider/model 设置。