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 gongyijie85/dsh-ecc --skill agent-self-evaluationgit clone --depth 1 https://github.com/gongyijie85/dsh-eccWrote 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/gongyijie85/dsh-ecc/agent-self-evaluation)<a href="https://agentmods.dev/skills/gongyijie85/dsh-ecc/agent-self-evaluation"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/agent-self-evaluation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gongyijie85/dsh-ecc/agent-self-evaluation"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/agent-self-evaluation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00060 | $0.01763 |
| Opus 5 | $0.00030 | $0.00881 |
| Sonnet 5 | $0.00012 | $0.00353 |
| Haiku 4.5 | $0.00006 | $0.00176 |
Grade A, and why
agent-self-evaluation 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 9d 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.
This is a copy
97% identical to agent-self-evaluation — 28 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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."
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.
- 9d ago First seen · 183 lines · 60 tokens per session scan A 96bb21eb3776
agent-self-evaluation is a skill published in the GitHub repository gongyijie85/dsh-ecc (6 stars, last pushed 2d ago), licensed MIT. It adds 60 tokens to every session and 1,763 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to agent-self-evaluation, differing in 28 lines, and is treated as a copy.
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