Atlas / Skills / affaan-m / Agent Self Evaluation

Agent Self EvaluationSAFE

skills/affaan-m/agent-self-evaluation

The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
284,444
01

Overview

The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

Read from source at commit 964473fab6bdOBSERVED · 2026-10-20
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02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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 errors
```

### The 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:

1. Read the axis question
2. Find evidence (or lack of evidence) in the output
3. Assign a score 1-5
4. 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:

1. State what you would do differently
2. If the gap is fixable in < 30 seconds (missing link, unclear phrasing), fix it now
3. 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]."

## 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
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codePASS
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-20 · audit v0.4.1 · source sha 964473fab6bdfull audit observations/trust-audit/skill/affaan-m__agent-self-evaluation.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-20964473fab6bdSAFEB89first audit
05

Questions

What does the agent-self-evaluation — affaan-m/everything-claude-code skill do?

The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

Is agent-self-evaluation — affaan-m/everything-claude-code safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can agent-self-evaluation — affaan-m/everything-claude-code access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (964473fab6bd), read on 2026-10-20. The repository is watched, and a new audit runs when it changes — this is the first audit.

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