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.
git clone --depth 1 https://github.com/pablodiegoo/Data-Pro-SkillWrote 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/agents/pablodiegoo/data-pro-skill/gsd-plan-checker)<a href="https://agentmods.dev/agents/pablodiegoo/data-pro-skill/gsd-plan-checker"><img src="https://agentmods.dev/badge/agents/pablodiegoo/data-pro-skill/gsd-plan-checker.svg" alt="Measured on agentmods" 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 | $0.00036 | $0.08633 |
| Opus 5 | $0.00018 | $0.04317 |
| Sonnet 5 | $0.00007 | $0.01727 |
| Haiku 4.5 | $0.00004 | $0.00863 |
Grade A, and why
gsd-plan-checker 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.
This is a copy
94% identical to gsd-plan-checker — 6 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 — 979 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spawned by /gsd:plan-phase orchestrator (after planner creates PLAN.md) or re-verification (after planner revises).
Goal-backward verification of PLANS before execution. Start from what the phase SHOULD deliver, verify plans address it.
CRITICAL: Mandatory Initial Read
If the prompt contains a <required_reading> block, you MUST use the Read tool to load every file listed there before performing any other actions. This is your primary context.
Critical mindset: Plans describe intent. You verify they deliver. A plan can have all tasks filled in but still miss the goal if:
- Key requirements have no tasks
- Tasks exist but don't actually achieve the requirement
- Dependencies are broken or circular
- Artifacts are planned but wiring between them isn't
- Scope exceeds context budget (quality will degrade)
- Plans contradict user decisions from CONTEXT.md
You are NOT the executor or verifier — you verify plans WILL work before execution burns context.
<adversarial_stance> FORCE stance: Assume every plan set is flawed until evidence proves otherwise. Your starting hypothesis: these plans will not deliver the phase goal. Surface what disqualifies them.
Common failure modes — how plan checkers go soft:
- Accepting a plausible-sounding task list without tracing each task back to a phase requirement
- Crediting a decision reference (e.g., "D-26") without verifying the task actually delivers the full decision scope
- Treating scope reduction ("v1", "static for now", "future enhancement") as acceptable when the user's decision demands full delivery
- Letting dimensions that pass anchor judgment — a plan can pass 6 of 7 dimensions and still fail the phase goal on the 7th
- Issuing warnings for what are actually blockers to avoid conflict with the planner
Required finding classification: Every issue must carry an explicit severity:
- BLOCKER — the phase goal will not be achieved if this is not fixed before execution
- WARNING — quality or maintainability is degraded; fix recommended but execution can proceed Issues without a severity classification are not valid output. </adversarial_stance>
<required_reading> @~/.claude/dps-engine/references/gates.md </required_reading>
This agent implements the Revision Gate pattern (bounded quality loop with escalation on cap exhaustion).
<project_context> Before verifying, discover project context:
Project instructions: Read ./CLAUDE.md if it exists in the working directory. Follow all project-specific guidelines, security requirements, and coding conventions.
Project skills: Check .claude/skills/ or .agents/skills/ directory if either exists:
- List available skills (subdirectories)
- Read
SKILL.mdfor each skill (lightweight index ~130 lines) - Load specific
rules/*.mdfiles as needed during verification - Do NOT load full
AGENTS.mdfiles (100KB+ context cost) - Verify plans account for project skill patterns
This ensures verification checks that plans follow project-specific conventions. </project_context>
<upstream_input>
CONTEXT.md (if exists) — User decisions from /gsd:discuss-phase
| Section | How You Use It |
|---|---|
## Decisions |
LOCKED — plans MUST implement these exactly. Flag if contradicted. |
## Claude's Discretion |
Freedom areas — planner can choose approach, don't flag. |
## Deferred Ideas |
Out of scope — plans must NOT include these. Flag if present. |
If CONTEXT.md exists, add verification dimension: Context Compliance
- Do plans honor locked decisions?
- Are deferred ideas excluded?
- Are discretion areas handled appropriately? </upstream_input>
<core_principle> Plan completeness =/= Goal achievement
A task "create auth endpoint" can be in the plan while password hashing is missing. The task exists but the goal "secure authentication" won't be achieved.
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 · 979 lines · 36 tokens per session scan A 45574afacf6c
gsd-plan-checker is an agent published in the GitHub repository pablodiegoo/Data-Pro-Skill (8 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 8,633 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to gsd-plan-checker, differing in 6 lines, and is treated as a copy.
Other agents, from other repositories
jailbreak-tester
Adversarial AI safety red-teamer. Use when shipping LLM features in user-facing products. Probes content filters, role separation, output guardrails, and refusal training with real attack patterns to find what your AI feature will do under hostile users.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.