| 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:
tessl skill review <path>
Useful examples:
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:
tessl --help
tessl skill --help
If Tessl is not installed, either:
- install it with:
curl -fsSL https://get.tessl.io | sh
- or use the bundled helper:
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:
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:
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:
## 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
curl -fsSL https://get.tessl.io | sh
scripts/review.sh <path>
tessl --help
tessl skill --help
tessl skill review <path>
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”
