Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Morningstar202604/awesome-skillkit --skill self-evalgit clone --depth 1 https://github.com/Morningstar202604/awesome-skillkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/morningstar202604/awesome-skillkit/self-eval)<a href="https://agentmods.dev/skills/morningstar202604/awesome-skillkit/self-eval"><img src="https://agentmods.dev/badge/skills/morningstar202604/awesome-skillkit/self-eval.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.01934 |
| Opus 5 | $0.00026 | $0.00967 |
| Sonnet 5 | $0.00010 | $0.00387 |
| Haiku 4.5 | $0.00005 | $0.00193 |
Grade A, and why
self-eval scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Eval: Honest Work Evaluation
ultrathink
Tier: STANDARD Category: Engineering / Quality Dependencies: None (prompt-only, no external tools required)
Description
Self-eval is a Claude Code skill that produces honest, calibrated work evaluations. It replaces the default AI tendency to rate everything 4/5 with a structured two-axis scoring system, mandatory devil's advocate reasoning, and cross-session anti-inflation detection.
The core insight: AI self-assessment converges to "everything is a 4" because a single-axis score conflates task difficulty with execution quality. Self-eval separates these axes, then combines them via a fixed matrix that the model cannot override.
Features
- Two-axis scoring — Independently rates task ambition (Low/Medium/High) and execution quality (Poor/Adequate/Strong), then combines via a lookup matrix
- Mandatory devil's advocate — Before finalizing, must argue for both higher AND lower scores, then resolve the tension
- Score persistence — Appends scores to
.self-eval-scores.jsonlin the working directory, building history across sessions - Anti-inflation detection — Reads past scores and flags clustering (4+ of last 5 identical)
- Matrix-locked scoring — The composite score comes from the matrix, not from direct selection. Low ambition caps at 2/5 regardless of execution quality
Usage
After completing work in a Claude Code session:
/self-eval
With context about what to evaluate:
/self-eval the authentication refactor we just completed
The skill reviews the conversation history, identifies what was accomplished, scores it on both axes, runs the devil's advocate check, and appends the result to the score history file.
Examples
Example 1: Feature Implementation
/self-eval added pagination to the user list API
Output:
## Self-Evaluation
**Task:** Added cursor-based pagination to the user list API endpoint
**Ambition:** Medium — New feature with established patterns, but required careful backwards compatibility
**Execution:** Strong — All tests pass, handles edge cases, existing clients unaffected
**Devil's Advocate:**
- Lower: Pagination is well-documented. The pattern was copied from another endpoint with minor changes. Could argue Low ambition.
- Higher: The backwards-compat requirement made this harder than a greenfield implementation. Had to support both offset and cursor modes.
- Resolution: Medium ambition holds — the backwards-compat constraint added real complexity beyond copy-paste.
**Score: 4** — Solid execution on meaningful work with real constraints.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 186 lines · 51 tokens per session scan A 0b829818fd15
self-eval is a skill published in the GitHub repository Morningstar202604/awesome-skillkit (1 stars, last pushed 3d ago), licensed Apache-2.0. It adds 51 tokens to every session and 1,934 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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