Borrowing it
Nothing to install: this file belongs to brake71/1c-ssl-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/brake71/1c-ssl-skills/main/.agents/skills/evaluation/SKILL.mdgit clone --depth 1 https://github.com/brake71/1c-ssl-skillsWrote 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/brake71/1c-ssl-skills/evaluation)<a href="https://agentmods.dev/skills/brake71/1c-ssl-skills/evaluation"><img src="https://agentmods.dev/badge/skills/brake71/1c-ssl-skills/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/brake71/1c-ssl-skills/evaluation"><img src="https://agentmods.dev/badge/skills/brake71/1c-ssl-skills/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 | $0.00040 | $0.03152 |
| Opus 5.5 | $0.00016 | $0.01261 |
| Sonnet 5.5 | $0.00008 | $0.00630 |
| Haiku 4.5 | $0.00004 | $0.00315 |
Grade A, and why
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 17d 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
100% identical to evaluation — 0 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 — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluation Methods for Agent Systems
Evaluate agent systems differently from traditional software because agents make dynamic decisions, are non-deterministic between runs, and often lack single correct answers. Build evaluation frameworks that account for these characteristics, provide actionable feedback, catch regressions, and validate that context engineering choices achieve intended effects.
When to Activate
Activate this skill when:
- Testing agent performance systematically
- Validating context engineering choices
- Measuring improvements over time
- Catching regressions before deployment
- Building quality gates for agent pipelines
- Comparing different agent configurations
- Evaluating production systems continuously
Do not activate this skill for adjacent work owned by other skills:
- Designing the LLM judge itself, pairwise comparison, judge calibration, or bias mitigation:
advanced-evaluation. - Designing autonomous control surfaces, novelty gates, rollback, or PR approval boundaries:
harness-engineering. - Debugging a specific context failure mode before measuring it:
context-degradation.
Core Concepts
Focus evaluation on outcomes rather than execution paths, because agents may find alternative valid routes to goals. Judge whether the agent achieves the right outcome via a reasonable process, not whether it followed a specific sequence of steps.
Use multi-dimensional rubrics instead of single scores because one number hides critical failures in specific dimensions. Capture factual accuracy, completeness, citation accuracy, source quality, and tool efficiency as separate dimensions, then weight them for the use case.
Use model-judged evaluation only after deterministic checks and rubrics are stable. When the work centers on judge prompts, pairwise comparison, calibration, or bias mitigation, switch to Advanced Evaluation.
Run deterministic validation before LLM judgment whenever the artifact has machine-checkable structure. Schema validity, duplicate keys, rubric math, manifest sync, retrieval status, and required evidence paths should fail fast before an evaluator spends tokens or returns a subjective score.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 17d ago First seen · 285 lines · 40 tokens per session scan A 1f31cda0910d
evaluation is a skill published in the GitHub repository brake71/1c-ssl-skills (82 stars, last pushed 15d ago), licensed MIT. It adds 40 tokens to every session and 3,152 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to evaluation, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
web-test
A browser-automation tool for testing the 1C:Enterprise web client. It uses Playwright to navigate sections, fill forms, read tables and reports, and perform user actions in a browser.
cfe-validate
A validator for 1C configuration extensions (CFE), which are add-ons that modify a 1C business configuration. It checks the extension’s XML structure, properties, inherited objects, and role permissions.
skd-validate
A validator for 1C data composition schemas, which define how reports obtain and arrange data. It checks a Template.xml file for structural errors, broken references, and duplicate names.
erf-validate
A validator for 1C external reports, which are report files added to the 1C business software platform.
xdto-validate
A validator for XDTO packages in 1C:Enterprise. It checks the package model, its metadata object, and its connection to the configuration.
v8-xsd-fetch
A command for downloading XSD schemas for versions of the 1C XML export format. XSD is a formal description used to check whether an XML file has the expected structure; 1C is a business software platform.