Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/mattgierhart/PRD-driven-context-engineeringnpx agentmods add skills/mattgierhart/prd-driven-context-engineering/prd-v08-runbook-creationWrote 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/mattgierhart/prd-driven-context-engineering/prd-v08-runbook-creation)<a href="https://agentmods.dev/skills/mattgierhart/prd-driven-context-engineering/prd-v08-runbook-creation"><img src="https://agentmods.dev/badge/skills/mattgierhart/prd-driven-context-engineering/prd-v08-runbook-creation/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/mattgierhart/prd-driven-context-engineering/prd-v08-runbook-creation"><img src="https://agentmods.dev/badge/skills/mattgierhart/prd-driven-context-engineering/prd-v08-runbook-creation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 196 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 209 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Excessive Agency · line 523 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 523 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 489 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00087 | $0.04331 |
| Opus 5 | $0.00044 | $0.02166 |
| Sonnet 5 | $0.00017 | $0.00866 |
| Haiku 4.5 | $0.00009 | $0.00433 |
Grade A, and why
prd-v08-runbook-creation scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- [ ] New version visible: curl https://api.prod.example.com/health | jq .version How it starts
The opening of the file, as written. The whole thing — 552 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Runbook Creation
Position in workflow: v0.8 Release Planning → v0.8 Runbook Creation → v0.8 Monitoring Setup
Execution Mode
Default is standard. See .claude/rules/08-skill-execution-modes.md for selection logic.
| Mode | What this skill produces |
|---|---|
| quick | 2–3 runbooks covering the top RISK-/MON- pairs |
| standard | Incident + deployment + maintenance runbooks linked to MON- and DEP-, with escalation paths |
| deep | Full matrix + drill schedule + recovery scenarios + tabletop exercise outputs |
Consumes
This skill requires prior work from v0.8 Release Planning and earlier stages:
- DEP-* deployment entries (from v0.8 Release Planning) — Deployment procedures from DEP- rollback/validation sections inform RUN- deployment and recovery runbooks
- RISK-* risk entries (from v0.5 Risk Discovery) — High/medium RISK- entries must have response runbooks; mitigations become procedures
- MON-* monitoring specifications (planned from v0.8 Monitoring Setup, or anticipated) — Key alerts from MON- (before formalization) inform incident response runbooks; runbooks will be referenced FROM monitoring
- ARC-* architecture decisions (from v0.6 Architecture Design) — System structure (single service vs microservices, databases, integrations) determines incident scope and escalation paths
- TECH-* technology stack (from v0.5 Technical Stack Selection) — Technology choices (database, cloud provider, APM tools) determine specific commands and tools in runbook procedures
- API-* endpoint contracts (from v0.6 Technical Specification) — For reference if incident involves specific endpoints or payloads
This skill assumes DEP- entries are complete with rollback procedures and post-deploy validation steps defined.
Produces
This skill creates/updates:
- RUN-* entries (operational runbooks, category-based) — Step-by-step procedures for incident response, deployment execution, maintenance tasks, recovery from failures, and escalation paths
- Incident response matrix — Mapping of scenarios (connection pool exhaustion, latency spike, deployment failure, etc.) to RUN- procedures
- Runbook cross-reference — Links from RUN- procedures to DEP- rollback conditions, anticipated MON- alerts, and RISK- entries they address
What ships with it
4 files 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.
- 10d ago First seen · 552 lines · 87 tokens per session scan A df0a4cc14bd4
prd-v08-runbook-creation is a skill published in the GitHub repository mattgierhart/PRD-driven-context-engineering (182 stars, last pushed 9d ago), licensed MIT. It adds 87 tokens to every session and 4,331 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
ralplan
Consensus planning entrypoint that auto-gates vague ralph/autopilot/team requests before execution.
remember
Review reusable project knowledge and decide what belongs in project memory, notepad, or durable docs.
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
docx-comment-reply
Reply to comments (批注) in Word .docx/.doc files: extract comment context, draft replies, write threaded replies back, and validate OOXML.
explaining-machine-learning-models
Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.
aiwg-regenerate-copilot
Regenerate copilot-instructions.md for GitHub Copilot with vendor-specific content only.