ai-feature-eval-harness

ai-feature-eval-harness is a skill for Claude Code from Mozurok/fhorja.dev. It costs 121 tokens per session (4,740 once invoked), scanned A, original, MIT.

A planning tool for evaluating an AI product feature, such as one that uses a language model, by defining success criteria, test data, grading methods, and a pass threshold. It saves the plan as AIEVALPLAN.md.

In plain words
What is it for?
Use it before or after shipping an AI feature to design a labelled evaluation dataset and decide which checks should be automated or judged for quality.
Why use it?
It replaces vague testing with a repeatable way to check whether model output meets defined requirements.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: reads .claude/ paths; reads ~/.codex paths; mentions Codex.

Good fit Use it before or after shipping an AI feature to design a labelled evaluation dataset and decide which checks should be automated or judged for quality.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mozurok/fhorja.dev/ai-feature-eval-harness
Install

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.

Any agent
npx skills add Mozurok/fhorja.dev --skill ai-feature-eval-harness
Clone the repo
git clone --depth 1 https://github.com/Mozurok/fhorja.dev

Made for: Claude Code.

Wrote 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.

agentmods badge for ai-feature-eval-harness

README.md
[![agentmods](https://agentmods.dev/badge/skills/mozurok/fhorja.dev/ai-feature-eval-harness/github.svg)](https://agentmods.dev/skills/mozurok/fhorja.dev/ai-feature-eval-harness)
Your own site
<a href="https://agentmods.dev/skills/mozurok/fhorja.dev/ai-feature-eval-harness"><img src="https://agentmods.dev/badge/skills/mozurok/fhorja.dev/ai-feature-eval-harness/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.

agentmods 80×15 button for ai-feature-eval-harness

Your own site · 80×15
<a href="https://agentmods.dev/skills/mozurok/fhorja.dev/ai-feature-eval-harness"><img src="https://agentmods.dev/badge/skills/mozurok/fhorja.dev/ai-feature-eval-harness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 121 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,740 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00121 $0.04740
Opus 5.5 $0.00048 $0.01896
Sonnet 5.5 $0.00024 $0.00948
Haiku 4.5 $0.00012 $0.00474

Measured 15d ago against content hash 79945f6b7c93, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ai-feature-eval-harness 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 15d 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.

.claude/skills/ai-feature-eval-harness/SKILL.md · 131 lines

How it starts

The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Output contract, in brief. This body is over the per-skill re-injection cap, so after a compaction the sections below are truncated away while this summary survives. They remain authoritative in full; re-read this file before emitting if you need them.

  • Standard output layout (required): Produce the command output using this structure (English only):
  • Artifact changes: Follow ## Global output contract in WORKFLOW_OPERATING_SYSTEM.md for APPLIED / PROPOSED / SKIP rules. Every listed file...
  • Command transcript: Brief audit trail (max 4 lines; max 3 in no-op runs with NO_OP_TRACE).
  • Handoff: Use the adaptive ending format of WORKFLOW_OPERATING_SYSTEM.md ## Global output contract. Every Handoff is one fenced text b...
  • Definition of done (command output): AI_EVAL_PLAN.md exists with measurable success criteria, a dataset spec (no train/eval leakage), a per-criterion grading tier, and...

Act as a senior AI evaluation engineer designing a repeatable eval plan for a product AI feature.

Goal: Define how to measure whether a model-backed feature actually works, before and after it ships, as a dataset-backed eval with explicit success criteria, a grading method per criterion, and a pass threshold. The load-bearing differentiator is that this measures a non-deterministic product output against a held-out labeled set, which deterministic functional tests cannot do. It is distinct from test-strategy (deterministic behavior tests) and from verify-against-rubric (which judges Fhorja's own command artifacts, not a user's product feature). It composes with ADR-0048: the code-graded tier banks Layer-1 evidence, and the LLM-graded tier adds signal for nuanced criteria a program cannot score.

Mandatory context bootstrap (before any output):

  • Read these sections in WORKFLOW_OPERATING_SYSTEM.md first:
    • ## LLM execution contract
    • ## Editor mode policy (mode definitions only; the tool mapping table is lazy-loaded in wos/editor-mode-mappings.md and needed only for non-Claude-Code tools)
    • ## Global output contract (including Adaptive handoff and Mode selection rule)
    • ## Cross-cutting workflow guardrails
  • Bootstrap tiers: the light-weight commands (branch-commit, what-next, where-we-at, slice-closure, compact-task-memory) plus the high-frequency implement-approved-slice and sync-task-state (v3 wave1 item D) read the four sections above with two subsections of ## Cross-cutting workflow guardrails skipped: ### External web access (centralized) and ### Sequencing heuristics (by phase). Everything else is read at every tier, including ### Proposal vs approved persistence and ### Substrate peer ownership (per ADR-0034), since all seven write substrate sections and reason about PROPOSED (state-reconcile stays on the full tier for cross-artifact judgment). The full tier is measured at 12109 tokens, the four always-read sections combined; the two skipped subsections are 1,034 of those (measured 2026-09-28), so the reduced tier is about 11,074. The leaf-reviewer tier (verify-against-rubric, ADR-0226) reads only ## Global output contract, measured at 5215 tokens, plus its rubric.
  • Session bootstrap reuse (skip-if-unchanged; v3 wave1 item D): WHEN this conversation already read the bootstrap sections in an earlier turn still VISIBLE in the context window AND WORKFLOW_OPERATING_SYSTEM.md has not changed since, the command MAY skip the re-read and cite the earlier one, emitting one Command transcript line: Bootstrap: reusing turn <N> read, WOS unchanged. Scoped exception to the context-budget re-fetch rule (wos/context-budget.md, "The re-fetch rule"), because these sections are one large, static, byte-identical read repeated every turn; every other tool result still re-fetches. VISIBLE means the section text itself is still present and quotable now, not merely that a record of the earlier read exists: a harness that clears a tool result while the record survives (ADR-0114) has not satisfied VISIBLE, and self-declared memory after a compaction never qualifies. A stateless-per-turn harness is excluded. The transcript line is mandatory; a silent skip is invalid output.
  • Resolving the spec and wos/<topic>.md: canonical repository FIRST. Otherwise use active-adapter docs: ~/.claude/workflow-docs (CLAUDE_WORKFLOW_DOCS_DIR), ~/.cursor/workflow-docs (WORKFLOW_DOCS_DIR; project .cursor/workflow-docs on project installs), ~/.codex/workflow-docs (CODEX_WORKFLOW_DOCS_DIR), or <kimi-home>/workflow-docs (KIMI_WORKFLOW_DOCS_DIR). Nonempty overrides win. Kimi home: KIMI_CODE_HOME, else existing ~/.kimi-code, else existing ~/.kimi, default ~/.kimi-code. Use one root for both paths; try other adapters if absent or unknown. Report resolved root, fallback to other adapters, or missing mandatory context in ### Command transcript. Installed snapshots refresh and prune retired topics only on sync.
  • Read additional sections only when relevant to this command's role.
  • Align all routing recommendations and next-command suggestions with the current command set.
  • Official next-command names only: recommendations, including Run now, use registered names from commands/<name>.md or commands/<name>/SKILL.md; installed mode uses the available Fhorja command or skill inventory. No invented aliases. Run now: none with Mode: N/A ends only if no honest next step exists, defined under ### Official command names (routing integrity) (ADR-0126). Required inputs:
  • active task folder path
  • TASK_STATE.md
  • SOURCE_OF_TRUTH.md
  • DECISIONS.md
  • IMPLEMENTATION_PLAN.md
  • the AI feature under eval: what it takes in, what the model produces, and the user-visible success condition
  • optional: an existing labeled dataset or golden set; when absent, the plan specifies how to build the first version
  • optional: a locked quality target (accuracy, pass rate, latency, cost per call) from DECISIONS.md or PROJECT_CHARTER.md or an SLA
  • last completed step from TASK_STATE.md (command + summary)

Read the full file on GitHub · 131 lines

Changes

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.

  1. 15d ago Changed · -1 lines · -84 tokens per session 79945f6b7c93
  2. 18d ago Changed 5de28a391321
  3. 25d ago Changed · -1 lines 442e9dfc7fca
  4. 26d ago Changed · -19 lines 49629b26ba94
  5. 1mo ago First seen · 152 lines · 205 tokens per session scan A 3fb9ca04d1fc

Subscribe to this mod's changes

ai-feature-eval-harness is a skill published in the GitHub repository Mozurok/fhorja.dev (10 stars, last pushed 15d ago), licensed MIT. It adds 121 tokens to every session and 4,740 once invoked, about $0.0005 per session on Opus 5.5. 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.

Related

Other skills, from other repositories

ai-evaluation-engineering

An AI evaluation engineering specialist for testing models and prompts. The description does not provide enough detail to state which concrete operations it performs.

devcodex-labs/devcodex · 95 tokens

sf-eval

Evaluate and benchmark Salesforce skill quality. Compares AI-generated code with vs without skill context, scores against a Salesforce-specific rubric (security, governor limits, bulkification, patterns, completeness), and produces a comparison report. Use to run benchmarks, verify skill value, or check Apex code…

Clientell-Ai/salesforce-skills · 93 tokens

data-quality-frameworks

Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.

wshobson/agents · 37 tokens

verification-before-completion

A checklist for verifying work before claiming that it is finished, fixed, built, tested, or ready to submit. It requires fresh command output and evidence for each claim.

jnMetaCode/superpowers-zh · 50 tokens

test-driven-development

Test-driven development, or TDD, is a way to build software by writing a test that fails, adding the smallest code that makes it pass, and then cleaning up the code. These instructions require that process for features, bug fixes, refactors, and behavior changes.

jnMetaCode/superpowers-zh · 20 tokens

go-testing

Trigger: Go tests, go test coverage, Bubbletea teatest, golden files. Apply focused Go testing patterns.

Gentleman-Programming/gentle-ai · 26 tokens