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 mattmre/EVOKORE-MCP-PUBLIC --skill sparc-specgit clone --depth 1 https://github.com/mattmre/EVOKORE-MCP-PUBLICWrote 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/mattmre/evokore-mcp-public/sparc-spec)<a href="https://agentmods.dev/skills/mattmre/evokore-mcp-public/sparc-spec"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/sparc-spec/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/mattmre/evokore-mcp-public/sparc-spec"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/sparc-spec.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 | $0.00046 | $0.00714 |
| Opus 5 | $0.00023 | $0.00357 |
| Sonnet 5 | $0.00009 | $0.00143 |
| Haiku 4.5 | $0.00005 | $0.00071 |
Grade A, and why
sparc-spec 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/sparc-spec — SPARC Specification Phase
Runs SPARC Phase 1: Specification. Produces a structured specification document with functional requirements, non-functional requirements, and Gherkin acceptance scenarios.
Usage
/sparc-spec "feature name"
/sparc-spec "Add user authentication with OAuth2"
What it does
- Elicits functional requirements from the feature description
- Derives non-functional requirements (performance, security, reliability)
- Writes Gherkin scenarios for happy path + top 3 edge cases
- Saves output to
docs/session-phases/phase_1_<slug>.md
Output Format
Creates docs/session-phases/phase_1_<slug>.md:
# Phase 1: Specification — {feature name}
**Generated:** {timestamp}
**SPARC Phase:** 1 of 5
**Slug:** {slug}
## Functional Requirements
| ID | Requirement | Acceptance Criteria |
|----|-------------|---------------------|
| FR-01 | {requirement} | {criteria} |
| FR-02 | ... | ... |
## Non-Functional Requirements
| ID | Category | Requirement | Threshold |
|----|----------|-------------|-----------|
| NFR-01 | Performance | {requirement} | {threshold} |
| NFR-02 | Security | ... | ... |
## Gherkin Acceptance Scenarios
### Happy Path
```gherkin
Feature: {feature name}
Scenario: {happy path description}
Given {precondition}
When {action}
Then {expected outcome}
Edge Cases
Scenario: {edge case 1}
Given {precondition}
When {action}
Then {expected outcome}
Done Criteria
- All FRs are testable (measurable acceptance criteria)
- NFRs have quantitative thresholds
- Gherkin covers happy path + 3 edge cases
- No ambiguous requirements ("fast", "secure" without thresholds)
Next Phase
Run /sparc-pipeline {feature name} to continue through all 5 phases, or start Phase 2 manually:
Create docs/session-phases/phase_2_{slug}.md with pseudocode for each FR from the spec.
## Done Criteria
The specification is complete when:
1. All functional requirements have measurable acceptance criteria
2. NFRs have explicit numeric thresholds (not "fast" — "p50 < 200ms")
3. At least one Gherkin scenario exists per FR
4. Edge cases cover: invalid input, concurrent access, resource limits
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.
- 7d ago First seen · 101 lines · 46 tokens per session scan A 6a49bd9c6f17
sparc-spec is a skill published in the GitHub repository mattmre/EVOKORE-MCP-PUBLIC (3 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 714 once invoked, about $0.0002 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.
Other skills, from other repositories
spec-linked-docs
Spec-Linked Documentation (SLD): Language-agnostic discipline for maintaining bidirectional traceability between functional specifications and source-code docstrings via stable identifiers and CI validation. Optional/opt-in adoption. Builds on OpenFastTrace and DO-178C Requirements Traceability Matrix traditions.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
verify-samples-tool
How to use the verify-samples tool to run, verify, and manage sample definitions in the Agent Framework repository. Use this when adding, updating, or running sample verification.
verify-dotnet-samples
How to build, run and verify the .NET sample projects in the Agent Framework repository. Use this when a user wants to verify that the samples still function as expected.
regex-tester
Validate, test, and debug regular expressions by executing them against sample inputs. Use when asked to build, verify, or explain a regex pattern.
design-qa-checklist
Build a QA checklist for verifying that a build matches the design. Use at implementation review. For the spec engineers build from, use handoff-spec.