Web Interface Guidelines Reviewer
vercel-labs/openreview
Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", or "check my…
Use after completing any non-trivial task. An agent skill from affaan-m/ECC.
Install Agent Self Evaluation
$ npx skills add affaan-m/ECC --skill agent-self-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install affaan-m/ECC agent-self-evaluation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/agent-self-evaluation .claude/skills/agent-self-evaluation && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "agent-self-evaluation" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/agent-self-evaluation into .claude/skills/agent-self-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-self-evaluation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/affaan-m/ECC/tree/main/skills/agent-self-evaluationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add affaan-m/ECC --skill agent-self-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install affaan-m/ECC agent-self-evaluation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/agent-self-evaluation .agents/skills/agent-self-evaluation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "agent-self-evaluation" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/agent-self-evaluation into .agents/skills/agent-self-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-self-evaluation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add affaan-m/ECC --skill agent-self-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install affaan-m/ECC agent-self-evaluation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/agent-self-evaluation .cursor/skills/agent-self-evaluation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "agent-self-evaluation" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/agent-self-evaluation into .cursor/skills/agent-self-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-self-evaluation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/affaan-m/ECC.git --path skills/agent-self-evaluation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add affaan-m/ECC --skill agent-self-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install affaan-m/ECC agent-self-evaluation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/agent-self-evaluation .gemini/skills/agent-self-evaluation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "agent-self-evaluation" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/agent-self-evaluation into .gemini/skills/agent-self-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-self-evaluation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install affaan-m/ECC agent-self-evaluationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add affaan-m/ECC --skill agent-self-evaluation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/agent-self-evaluation .github/skills/agent-self-evaluation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "agent-self-evaluation" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/agent-self-evaluation into .github/skills/agent-self-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-self-evaluation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add affaan-m/ECC --skill agent-self-evaluation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install affaan-m/ECC agent-self-evaluation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/agent-self-evaluation .opencode/skills/agent-self-evaluation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "agent-self-evaluation" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/agent-self-evaluation into .opencode/skills/agent-self-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-self-evaluation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
agent-self-evaluation- GitHub stars
- 276k
- Token cost
- ~1.9k tokens
- SKILL.md length
- 665 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 673
- Repo updated
- First seen
- Licence
- MIT
At a glance
Use after completing any non-trivial task. An agent skill from affaan-m/ECC.
- Works in 4 steps: Collect the Raw Material → Score Each Axis Independently → Produce the Evaluation Report → …
- Tasks that involve Accessibility
- SKILL.md covers When to Activate, Core Concepts, Workflow and Code Examples, plus 3 more sections
- Runs Python scripts from its folder
What it does
Agent Self Evaluation is an agent skill from affaan-m/ECC. Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `examples/high-score-example.md`, `examples/low-score-example.md` and `references/evaluation-criteria.md`).
It sits in Frontend & Design, covering Accessibility. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.
When your agent uses it
- Tasks that involve Accessibility
Example prompts
- “/agent-self-evaluation”
Requirements
- Python 3
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit ef648e0. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Agent Self Evaluation loads about 1.9k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 71 tokens; SKILL.md has 665 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from affaan-m/ECC at commit ef648e0, republished under its MIT licence (© affaan-m). 665 words, ~1,890 tokens.
Download SKILL.mdSave it as
.claude/skills/agent-self-evaluation/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- agent-self-evaluation
- description
- Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.
- origin
- ECC
Agent Self-Evaluation
After completing a complex task, the agent pauses to rate its own output against a structured 5-axis rubric. This is NOT a pass/fail gate — it's a deliberate reflection step that catches omissions, flags overconfidence, and surface areas for improvement before the user has to.
When to Activate
- After writing code that spans 3+ files or 50+ lines
- After completing a multi-step workflow (implement → test → review)
- After a debugging session that involved 3+ attempts
- After producing a design document, architecture decision, or written analysis
- When the user asks "how good was that?" or "rate yourself"
- At the end of any session Stop hook (if configured — see
references/hook-integration.md)
Core Concepts
The 5 Evaluation Axes
| Axis | Question | What it catches |
|---|---|---|
| Accuracy | Are the facts, claims, and outputs correct? | Hallucinations, wrong API names, incorrect syntax, false statements |
| Completeness | Did it cover everything the user asked for? | Missed edge cases, unhandled error paths, forgotten requirements, skipped subtasks |
| Clarity | Is the explanation understandable and well-structured? | Confusing explanations, jargon without definition, missing context, rambling |
| Actionability | Can the user act on the output immediately? | Vague suggestions, missing steps, "you should X" without showing how, no verification path |
| Conciseness | Did it use the minimum words/tokens needed? | Redundancy, over-explanation, repeating the user's question verbatim, filler content |
Scoring Scale
5 — Exceptional: no reasonable improvement possible
4 — Good: minor nits only, no substantive gaps
3 — Adequate: meets the request but has a notable weakness on at least one axis
2 — Weak: has a clear gap that affects usability or correctness
1 — Poor: fundamentally misses the request or contains significant errorsThe Evidence Rule
Every score below 5 MUST cite specific evidence. A score of 3 cannot just say "could be better" — it must say exactly what is missing or wrong. The mantra: "Show the gap, don't just name it."
Workflow
Step 1: Collect the Raw Material
Gather what you'll evaluate:
- The original user request (read back from conversation)
- Your final response/output (the deliverable)
- Any tool outputs that verify correctness (test results, exit codes, lint output)
- Any user feedback received during the task (corrections, "try again", "that's not right")Step 2: Score Each Axis Independently
Work through the 5 axes one at a time. For each:
- Read the axis question
- Find evidence (or lack of evidence) in the output
- Assign a score 1-5
- If score < 5, write a one-sentence improvement note citing the gap
Do NOT average the scores in your head first and then work backwards. Score each axis fresh.
Step 3: Produce the Evaluation Report
Use the template from templates/evaluation-report.md. The report must include:
- One-line summary
- 5-axis scorecard (score + evidence per axis)
- Overall score (simple average, rounded to 1 decimal)
- 1-3 specific improvements ranked by impact
- Self-check: "Would the user agree with this assessment?"Step 4: Apply the Improvement
If any axis scored 3 or below:
- State what you would do differently
- If the gap is fixable in < 30 seconds (missing link, unclear phrasing), fix it now
- If the gap requires rework, flag it explicitly: "This axis scored [reason] because [evidence]. Re-running with [specific fix] would likely raise it to [score]."
Show full SKILL.md (264 more words)Show less
Code Examples
Example: Good Evaluation (Score 4+)
Task: Add retry logic to HTTP client
Scorecard:
Accuracy: 5 — All API calls correct. Verified: retries use
exponential backoff. No hallucinated methods.
Completeness: 4 — Covered happy path + 3 error cases. Missing:
timeout handling for hung connections.
Clarity: 5 — Code comments explain backoff formula.
PR description links to incident that motivated this.
Actionability:5 — Single merge. No follow-up tasks. Tests pass.
Conciseness: 4 — 47 lines total. The retry loop could be extracted
into a helper to drop ~8 lines.
Overall: 4.6 — One gap (timeout handling). Fix before merging.Example: Weak Evaluation (Score 2-3)
Task: Add retry logic to HTTP client
Scorecard:
Accuracy: 2 — Used urllib3 which doesn't match our
httpx-based codebase. Wrong library.
Completeness: 3 — Works for GET. POST/PUT not handled (user
said "all HTTP requests").
Clarity: 4 — Code is readable. Good variable names.
Actionability:2 — "Add tests" mentioned but no test file created.
User has to write tests before merging.
Conciseness: 3 — 120 lines. The retry config is duplicated in
3 places instead of one shared RetryConfig object.
Overall: 2.8 — Wrong library used. Needs httpx rewrite.
Fix accuracy first (switch to httpx), then extend to all
HTTP methods, then consolidate config.Anti-Patterns
"Everything is a 5"
FAIL: Accuracy: 5 — All good.
Completeness: 5 — Everything covered.
Clarity: 5 — Clear.No evidence cited. This is self-congratulation, not evaluation. A real 5 requires proving there's nothing to improve.
Over-penalizing for scope creep
FAIL: Completeness: 2 — Didn't handle WebSocket connections or
gRPC streaming (user didn't ask for these)Only evaluate against what the user actually requested, not what you could have additionally built.
Using the evaluation to re-litigate
FAIL: "As I said earlier, this approach is wrong. Score: 1"The evaluation is about the delivered output, not about re-arguing design decisions that were already made. If the approach was wrong, that should have been caught before delivery.
Mixing personal preference with objective gaps
FAIL: "Score: 3. I don't like Python decorators.""Don't like" is not evidence. Cite a concrete readability, testability, or correctness concern, or leave the score at 4+.
Best Practices
- Evaluate the output, not the process. The user cares about what you delivered, not how many iterations you took.
- One improvement per weak axis. Don't list 5 things for one axis — pick the highest-impact gap.
- Tie improvements to user impact. "Missing error handling means the user's API call will crash silently" beats "add error handling."
- Be specific about what 'fixed' looks like. "Re-run with httpx transport configured for retries" beats "fix the library issue."
- Use tool outputs as evidence. If tests passed, cite them. If lint is clean, cite it. Don't guess — grep for the proof.
- If you can't find any gaps, try harder. A perfect score across all 5 axes is rare. Ask: "If I were the user, what would annoy me about this output?"
Related Skills
agent-eval— Head-to-head comparison of different coding agents on benchmark tasksverification-loop— Systematic verification of outputs against expected resultssecurity-review— Security-focused code review checklist
© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 6 other files (scripts, references) in skills/agent-self-evaluation of affaan-m/ECC.
- SKILL.md
- examples/high-score-example.md
- examples/low-score-example.md
- references/evaluation-criteria.md
- references/hook-integration.md
- scripts/evaluate.py
- templates/evaluation-report.md
Open the folder on GitHubat commit ef648e0
Compare with similar skills
Agent Self Evaluation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Agent Self Evaluation this skillaffaan-m/ECC | 276k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Web Interface Guidelines Reviewervercel-labs/openreview | 1.7k | 97 repos | ~308 | Automated safety check: Pass | None | |
| Accessibility Reviewmarkmead/hyperui | 12k | 1 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Web Animation DesignbaptisteArno/typebot.io | 11k | 2 repos | ~2.7k | Automated safety check: Pass | Custom licence | |
| Accessibility Fixeribelick/ui-skills | 9.5k | 4 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Wcag Audit PatternsvmDeshpande/ai-agent-automation | 178 | 11 repos | ~610 | Automated safety check: Pass | Apache-2.0 |
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Categories
Questions about Agent Self Evaluation
What does Agent Self Evaluation do?
Use after completing any non-trivial task. An agent skill from affaan-m/ECC. Agent Self Evaluation is an agent skill from affaan-m/ECC. Use after completing any non-trivial task.
When should I use Agent Self Evaluation?
Agent Self Evaluation fits situations like: tasks that involve Accessibility.
How do I install Agent Self Evaluation in Claude Code?
Run `npx skills add affaan-m/ECC --skill agent-self-evaluation -a claude-code`. Or copy the skill folder (skills/agent-self-evaluation in affaan-m/ECC) into .claude/skills/agent-self-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Agent Self Evaluation in Codex?
Run `npx skills add affaan-m/ECC --skill agent-self-evaluation -a codex`. Or copy the skill folder (skills/agent-self-evaluation in affaan-m/ECC) into .agents/skills/agent-self-evaluation in your project. Codex loads it when a task matches its description.
Can I use Agent Self Evaluation in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill agent-self-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-self-evaluation, .gemini/skills/agent-self-evaluation, .github/skills/agent-self-evaluation and .opencode/skills/agent-self-evaluation in your project.
What does Agent Self Evaluation need to run?
Going by SKILL.md and its folder, Agent Self Evaluation needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Agent Self Evaluation access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Agent Self Evaluation safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Agent Self Evaluation use?
Agent Self Evaluation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Agent Self Evaluation use?
About 1.9k tokens (SKILL.md is roughly 7.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.1k tokens, read only when the agent opens those files.
What are the alternatives to Agent Self Evaluation?
Skills that share tags, products or a category with Agent Self Evaluation: Web Interface Guidelines Reviewer (vercel-labs/openreview, 1.7k stars), Accessibility Review (markmead/hyperui, 12k stars), Web Animation Design (baptisteArno/typebot.io, 11k stars) and Accessibility Fixer (ibelick/ui-skills, 9.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Agent Self Evaluation?
affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 275,546 GitHub stars. The repository holds 673 skills in this directory. The repository was last updated on October 5, 2026.
Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.