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goal-evaluator

goal-evaluator is a skill for Claude Code from synaptiai/synapti-marketplace. It costs 94 tokens per session (1,400 once invoked), scanned A, original, Apache-2.0.

A goal-checking workflow that runs defined verification commands, records their evidence, and assigns a result such as complete, incomplete, failed, or blocked.

In plain words
What is it for?
It helps evaluate active goals against their acceptance criteria and update their lifecycle status.
Why use it?
It makes automated checks take priority over subjective judgment and exposes goals that lack enough proof.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: agent in frontmatter; names the AskUserQuestion tool.

Part of the flow plugin — 32 skills, 21 commands, 9 agents shipped together

not rated 6repo changed yesterday A scan 94 tokens original Apache-2.0

Good fit It helps evaluate active goals against their acceptance criteria and update their lifecycle status.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/synaptiai/synapti-marketplace/goal-evaluator
View source ↗ synaptiai/synapti-marketplace
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 synaptiai/synapti-marketplace --skill goal-evaluator
Clone the repo
git clone --depth 1 https://github.com/synaptiai/synapti-marketplace

Made for: Claude Code.

Or install flow, the plugin that ships this one along with the rest of its 32 skills, 21 commands, 9 agents.

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 goal-evaluator

README.md
[![agentmods](https://agentmods.dev/badge/skills/synaptiai/synapti-marketplace/goal-evaluator/github.svg)](https://agentmods.dev/skills/synaptiai/synapti-marketplace/goal-evaluator)
Your own site
<a href="https://agentmods.dev/skills/synaptiai/synapti-marketplace/goal-evaluator"><img src="https://agentmods.dev/badge/skills/synaptiai/synapti-marketplace/goal-evaluator/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 goal-evaluator

Your own site · 80×15
<a href="https://agentmods.dev/skills/synaptiai/synapti-marketplace/goal-evaluator"><img src="https://agentmods.dev/badge/skills/synaptiai/synapti-marketplace/goal-evaluator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 94 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,400 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 $0.00094 $0.01400
Opus 5 $0.00047 $0.00700
Sonnet 5 $0.00019 $0.00280
Haiku 4.5 $0.00009 $0.00140

Measured yesterday against content hash afb43866c626, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

goal-evaluator 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 yesterday.

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.

plugins/flow/skills/goal-evaluator/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Goal Evaluator

Contract

Iron law: deterministic checks beat LLM judgment: run every verification_command first and never substitute judge output for a runnable command's exit code. Invoked by /flow:goal evaluate <id> and /flow:debug step 6 (trigger=command), and by hooks/scripts/flow-goal-evaluator.sh in evaluator-loop mode (trigger=stop-hook), with goal id, run id, and trigger. Returns {verdict, confidence, delta, reason, next_step_hint, criterion_results} plus, for terminal outcomes, a proposed_transition; writes evidence sidecars and non-terminal lifecycle updates, never last-verdict.json and never a terminal status. Permitted skips: the judge when no fuzzy criteria remain; the path-boundary check when constraints.allowed_paths is unset.

Inputs

  1. Goal id: .flow/goals/<id>.goal.yaml with status active (or resumable waiting_for_user, waiting_for_ci, blocked).
  2. Run id: for .flow/runs/<run-id>/evidence/; defaults to the goal's scope.run_id.
  3. Trigger: manual | stop-hook | command.

Outputs

  1. Updated goal: AC status (pending → evidence_collected → pass | fail), evidence_ref, last_evaluated_at, last_result; lifecycle.last_evaluation; non-terminal lifecycle.status.
  2. *.evidence.yaml sidecars via bin/flow-record-evidence.sh (the goal-evidence-ledger skill).
  3. A goal-evaluation journal artifact.
  4. The structured verdict, returned to the caller.

Workflow

Step 1: Load

Read the goal, confirm it matches schemas/v1/goal.schema.json, and read existing sidecars for ACs that already carry evidence_ref.

Step 2: Deterministic checks

For each AC with a verification_command: run bash -c "<command>", capture stdout/stderr and the exit code, write a command_result FlowEvidence with proves: [<AC.id>] and limitations (criterion-verification-map's "Does NOT promise" field when present), then update the AC entry (status: evidence_collected, evidence_ref, last_evaluated_at, last_result).

Read the full file on GitHub · 80 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. yesterday Changed · -81 lines · -49 tokens per session afb43866c626
  2. 8d ago First seen · 161 lines · 143 tokens per session scan A cbf82fba6da6

Subscribe to this mod's changes

goal-evaluator is a skill published in the GitHub repository synaptiai/synapti-marketplace (6 stars, last pushed today), licensed Apache-2.0. It adds 94 tokens to every session and 1,400 once invoked, about $0.0005 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 2026-09-03.

PlanningTestingagentic-design-patternsagentic-developmentagentic-workflowai-developer-toolsai-firstclaude-code-plugin
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