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 zhaono1/agent-playbook --skill prd-implementation-precheckgit clone --depth 1 https://github.com/zhaono1/agent-playbookWrote 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/zhaono1/agent-playbook/prd-implementation-precheck)<a href="https://agentmods.dev/skills/zhaono1/agent-playbook/prd-implementation-precheck"><img src="https://agentmods.dev/badge/skills/zhaono1/agent-playbook/prd-implementation-precheck.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk pass
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 | $0.00028 | $0.01026 |
| Opus 5 | $0.00014 | $0.00513 |
| Sonnet 5 | $0.00006 | $0.00205 |
| Haiku 4.5 | $0.00003 | $0.00103 |
Grade A, and why
prd-implementation-precheck 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PRD Implementation Precheck
Overview
Perform a short PRD precheck, surface material issues, then implement. Ask the user only when a missing choice would materially change behavior, architecture, data, or external side effects.
Workflow
- Locate the PRD and any referenced files.
- Precheck the PRD and summarize intent in 1-2 sentences.
- List findings and questions with blockers first. If there are no material blockers, state assumptions and continue.
- When a blocker exists, resolve it with the user before implementation.
- Validate (tests or manual steps) or state what was not run.
Precheck Checklist
Basic Checks
- Scope: Identify over-broad changes; suggest a smaller, targeted approach.
- Alignment: Flag conflicts with existing patterns or architecture; propose alternatives.
- Dependencies: Note missing hooks/providers/data sources or unclear ownership.
- Behavior: Verify flows and edge cases are specified; ask for gaps.
- Risks: Call out performance, regressions, or migration risks.
- Testing: Check success criteria and test coverage; request specifics if vague.
Edge Case Coverage Checks
Verify the PRD addresses these edge cases (mark as ⚠️ if missing):
Data Boundaries
- Null/Empty handling - What happens with empty inputs or null values?
- Boundary values - Are min/max limits defined? What happens at boundaries?
- Duplicate data - How are duplicates detected and handled?
- Data format - Are input formats validated? What about special characters?
State Boundaries
- State transitions - Are all valid state transitions defined?
- Invalid transitions - What happens on illegal state changes?
- Concurrent modifications - How are simultaneous edits handled?
- Rollback scenarios - Can operations be undone? How?
Error Boundaries
- Network failures - What happens when API calls fail?
- Timeout behavior - Are timeouts defined? What's the retry strategy?
- Partial failures - If step 2 of 3 fails, what happens to step 1?
- Error messages - Are user-facing error messages defined?
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 124 lines · 28 tokens per session scan A f63611993ebe
prd-implementation-precheck is a skill published in the GitHub repository zhaono1/agent-playbook (77 stars, last pushed 12d ago), licensed MIT. It adds 28 tokens to every session and 1,026 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.