Agent skill

Fidelity Validation

by prime-radiant-inc in prime-radiant-inc/greenfield

Cross-validates sanitized output specs against raw source specs to detect lost behavioral detail, dropped constants, missing features, or diluted precision.

Apache-2.0Auto-check passed

Install Fidelity Validation

skills CLI
$ npx skills add prime-radiant-inc/greenfield --skill fidelity-validation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install prime-radiant-inc/greenfield fidelity-validation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/prime-radiant-inc/greenfield.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/fidelity-validation .claude/skills/fidelity-validation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
fidelity-validation
GitHub stars
292
Token cost
~1.8k tokens
SKILL.md length
899 words
Files
1
Skills in repo
21
Repo updated
First seen
Licence
Apache-2.0

At a glance

Cross-validates sanitized output specs against raw source specs to detect lost behavioral detail, dropped constants, missing features, or diluted precision.

  • Works in 5 steps: Build the Source Claim Inventory → Cross-Validate Against Output → Also Validate Cross-Cutting Artifacts → …
  • SKILL.md covers Why This Exists, When to Run, The Principle and Process, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Fidelity Validation is an agent skill from prime-radiant-inc/greenfield. Cross-validates sanitized output specs against raw source specs to detect lost behavioral detail, dropped constants, missing features, or diluted precision. Run AFTER sanitization and AFTER contamination audit passes.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: A Claude Code plugin that reverse-engineers clean behavioral specs, test vectors, and acceptance criteria from any codebase, producing a provenance trail so a fresh team can… The licence is Apache-2.0.

Example prompts

  • “/fidelity-validation”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Build the Source Claim Inventory
  2. Cross-Validate Against Output
  3. Also Validate Cross-Cutting Artifacts
  4. Severity Assessment
  5. Write Report

What it can do on your machine

Read from SKILL.md and the folder at commit 6e6d4b4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fidelity Validation loads about 1.8k tokens when it runs. Until then it costs about 59 tokens; SKILL.md has 899 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~59
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from prime-radiant-inc/greenfield at commit 6e6d4b4, republished under its Apache-2.0 licence (© prime-radiant-inc). 899 words, ~1,830 tokens.

Download SKILL.mdSave it as .claude/skills/fidelity-validation/SKILL.md (or your agent's skills folder).
name
fidelity-validation
description
Cross-validates sanitized output specs against raw source specs to detect lost behavioral detail, dropped constants, missing features, or diluted precision. Run AFTER sanitization and AFTER contamination audit passes.

Fidelity Validation

Sanitization removes source code identifiers. But aggressive sanitization can also remove behavioral detail that an implementor needs. This skill detects information loss.

Why This Exists

The sanitization pass rewrites raw specs into output specs. Each rewrite risks:

  • Dropped behaviors: A feature or edge case described in the raw specs that doesn't appear in the output specs
  • Lost constants: A numeric threshold, timeout, limit, or size that was accidentally removed or changed during rewriting
  • Diluted precision: A specific behavioral rule replaced with vague language ("the system handles errors" instead of "the system retries with configured backoff — N attempts, base delay, maximum delay, and jitter")
  • Missing decision trees: A conditional behavior with multiple branches that was simplified to just the happy path
  • Dropped error conditions: Error handling that was described in the raw specs but omitted from the output specs
  • Feature gaps: Entire features or sub-features present in the raw specs but missing from the output domain spec

When to Run

Run this AFTER:

  1. Sanitization (Layer 5) is complete
  2. Contamination audit (Layer 6) passes — output specs are confirmed free of source identifiers
  3. All remediation/rewriting rounds are done

This is the final quality gate before handing the output to the implementer.

The Principle

Every behavioral claim in the raw specs must have a corresponding claim in the output specs — with equal or greater precision.

The output specs may use different words (that's the point of sanitization), but it must convey the same behavior. If the raw spec says "retry N times with a specific base, cap, and jitter", the output specs must say the same — not just "the system retries on failure."

Process

Phase 1: Build the Source Claim Inventory

For each raw module spec (workspace/raw/specs/modules/*.md), extract:

  1. All numeric constants — timeouts, limits, sizes, counts, thresholds, intervals, percentages
  2. All behavioral rules — "when X happens, the system does Y"
  3. All error conditions — what errors occur and how they're handled
  4. All state transitions — state machines, mode changes, lifecycle events
  5. All decision trees — if/else branches, priority orders, cascades
  6. All features — distinct capabilities described

Write this inventory to workspace/raw/audit/fidelity-inventory.md.

Phase 2: Cross-Validate Against Output

For each item in the source claim inventory, search the corresponding output domain spec(s) for a matching behavioral claim.

Match criteria:

  • The output specs describes the same behavior (possibly with different words)
  • Numeric constants match exactly (no rounding, no approximation)
  • Decision branches are all present (not just the happy path)
  • Error conditions are all present
  • State transitions are complete

Report format per item:

### [RAW-SPEC: section-name] Claim: "description of the behavioral claim"
- Constant/Rule/Error/State/Decision/Feature
- Raw value: [exact value from raw specs]
- Output location: [file:section where it appears in the output] or MISSING
- Output value: [exact value from output specs] or N/A
- Status: MATCH | WEAKENED | MISSING | CHANGED
- Notes: [if WEAKENED/MISSING/CHANGED, explain what was lost]

Status definitions:

  • MATCH: Output spec conveys the same behavior with the same precision
  • WEAKENED: Output spec mentions the behavior but with less precision (e.g., "retries on failure" instead of "retries with specific backoff params")
  • MISSING: Output spec does not mention this behavior at all
  • CHANGED: Output spec describes different behavior for the same scenario
Phase 3: Also Validate Cross-Cutting Artifacts

Repeat Phase 2 for:

  • Journey specs: raw journeys vs output journeys
  • Contracts: raw contracts vs output contracts
  • Protocol specs: raw protocols vs output protocols
  • Test vectors: raw test vectors vs output test vectors (constants must match)
  • Acceptance criteria: raw ACs vs output ACs (criteria must be present)
Show full SKILL.md (372 more words)Show less
Phase 4: Severity Assessment

Categorize all WEAKENED, MISSING, and CHANGED findings:

SeverityCriteriaAction
P0-CRITICALA P0 behavior (critical path) is MISSING or CHANGEDMust fix before implementation
P0-WEAKENEDA P0 behavior lost precision (constants, branch coverage)Must fix before implementation
P1-MISSINGA P1 behavior is entirely absent from the outputShould fix
P1-WEAKENEDA P1 behavior lost precisionShould fix
P2-MISSINGA P2/P3 behavior is absentAdvisory — may fix
P2-WEAKENEDA P2/P3 behavior lost precisionAdvisory — may fix
Phase 5: Write Report

Write the full report to workspace/raw/audit/fidelity-report.md.

The report SHALL include:

  1. Summary statistics: total claims checked, MATCH count, WEAKENED count, MISSING count, CHANGED count
  2. P0-CRITICAL and P0-WEAKENED findings: full details with exact raw vs output text
  3. P1 findings: full details
  4. P2 findings: summary only
  5. Verdict: PASS (zero P0 issues) or FAIL (any P0 issue exists)

Write a summary to workspace/output/audit/fidelity-summary.md:

  • Verdict: PASS or FAIL
  • Total claims validated
  • Match rate percentage
  • Number of issues by severity
  • NO details about what was found or what raw specs say — this is an implementer-facing artifact

Agent Dispatch Pattern

The fidelity validation is compute-heavy (reading all raw specs AND all output specs). Dispatch as:

  1. Inventory builders (parallel, one per 4-5 raw module specs): Extract behavioral claims from raw specs → workspace/raw/audit/fidelity-inventory-batch-N.md

  2. Cross-validators (parallel, one per output domain spec): Read the relevant inventory batches + the output domain spec → produce per-domain findings

  3. Aggregator (sequential): Merge all findings → severity assessment → final report

Each agent is dispatched as greenfield:analyzer since they need to read both raw/ and output/.

What This Does NOT Check

  • Contamination (that's the Layer 6 audit's job)
  • Structural organization (output specs may organize differently than raw — that's fine)
  • Prose quality (rewording is expected)
  • Provenance citations (these are intentionally stripped)

Relationship to Other Skills

  • spec-sanitization: Creates the output specs this skill validates
  • second-pass-review: Checks for contamination; this skill checks for information loss
  • validation-methodology: Defines the acceptance criteria that both raw and output specs must satisfy
  • behavioral-spec-writing: Defines what behavioral claims look like

Together, the audit (Layer 6) and fidelity validation form a two-sided check:

  • Layer 6 asks: "Is there anything in output/ that shouldn't be?" (contamination)
  • Fidelity validation asks: "Is there anything NOT in output/ that should be?" (information loss)

© prime-radiant-inc, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/fidelity-validation of prime-radiant-inc/greenfield.

Open the folder on GitHubat commit 6e6d4b4

Compare with similar skills

Fidelity Validation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Fidelity Validation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Fidelity Validation this skillprime-radiant-inc/greenfield292—~1.8kAutomated safety check: PassApache-2.0
Spec Writergarrytan/gstack136k—~14kAutomated safety check: NotesMIT
Form Validationthedaviddias/Front-End-Checklist74k—~633Automated safety check: PassMIT
Input Validation And Sanitizationaiming-lab/MetaClaw3.5k—~251Automated safety check: PassMIT
Specgarden-co/classic-jazz2.5k—~1.3kAutomated safety check: PassMIT
Validateagenticnotetaking/arscontexta3.5k—~3kAutomated safety check: PassMIT

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Questions about Fidelity Validation

What does Fidelity Validation do?

Cross-validates sanitized output specs against raw source specs to detect lost behavioral detail, dropped constants, missing features, or diluted precision. Fidelity Validation is an agent skill from prime-radiant-inc/greenfield. Cross-validates sanitized output specs against raw source specs to detect lost behavioral detail, dropped constants, missing features, or diluted precision.

How do I install Fidelity Validation in Claude Code?

Run `npx skills add prime-radiant-inc/greenfield --skill fidelity-validation -a claude-code`. Or copy the skill folder (skills/fidelity-validation in prime-radiant-inc/greenfield) into .claude/skills/fidelity-validation in your project. Claude Code loads it when a task matches its description.

How do I install Fidelity Validation in Codex?

Run `npx skills add prime-radiant-inc/greenfield --skill fidelity-validation -a codex`. Or copy the skill folder (skills/fidelity-validation in prime-radiant-inc/greenfield) into .agents/skills/fidelity-validation in your project. Codex loads it when a task matches its description.

Can I use Fidelity Validation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add prime-radiant-inc/greenfield --skill fidelity-validation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fidelity-validation, .gemini/skills/fidelity-validation, .github/skills/fidelity-validation and .opencode/skills/fidelity-validation in your project.

What does Fidelity Validation need to run?

SKILL.md names no scripts, command-line tools or credentials: Fidelity Validation is instructions for the agent only.

Does Fidelity Validation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Fidelity Validation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Fidelity Validation use?

Fidelity Validation is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fidelity Validation use?

About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Fidelity Validation?

Skills that share tags, products or a category with Fidelity Validation: Spec Writer (garrytan/gstack, 136k stars), Form Validation (thedaviddias/Front-End-Checklist, 74k stars), Input Validation And Sanitization (aiming-lab/MetaClaw, 3.5k stars) and Spec (garden-co/classic-jazz, 2.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fidelity Validation?

prime-radiant-inc (a GitHub organization) maintains it in prime-radiant-inc/greenfield, which has 292 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on August 6, 2026.

Source: prime-radiant-inc/greenfield on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.