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 ashermahonin/agentic-skills --skill ai-pdlcgit clone --depth 1 https://github.com/ashermahonin/agentic-skillsWrote 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/ashermahonin/agentic-skills/ai-pdlc)<a href="https://agentmods.dev/skills/ashermahonin/agentic-skills/ai-pdlc"><img src="https://agentmods.dev/badge/skills/ashermahonin/agentic-skills/ai-pdlc/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/ashermahonin/agentic-skills/ai-pdlc"><img src="https://agentmods.dev/badge/skills/ashermahonin/agentic-skills/ai-pdlc.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 | $0.00066 | $0.00857 |
| Opus 5 | $0.00033 | $0.00428 |
| Sonnet 5 | $0.00013 | $0.00171 |
| Haiku 4.5 | $0.00007 | $0.00086 |
Grade A, and why
ai-pdlc 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 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.
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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI PDLC
Purpose
Organize uncertain product work around evidence. sdlc-orchestrator chooses the operational route; this skill records what each product phase has established, what remains a hypothesis, and what should be tested next.
Inputs
- Read
references/pdlc-phases.md. - Pull the product intent and constraints from
intake-coordinator. - Pull existing research from
research-domainandcompetitive-analysis(if present); request them if missing. - Restate the project as 3–5 falsifiable hypotheses, each with a "kill" criterion and a cheapest-useful-evidence step.
- Use Context7 MCP to validate any external technology, market, or platform-policy claim that gates a hypothesis.
Decision process
- Phase 0 — Define. Lock product intent, success metric, primary user, and platform matrix (with
platform-detector). - Phase 1 — Discover. Run
research-domain+competitive-analysis. Record what is known and what is still guess. - Phase 2 — Hypothesize. Use
hypothesis-validatorto express each top design choice as a falsifiable claim with a measurable kill criterion. - Phase 3 — Prototype. Build the smallest artifact that disproves or supports the riskiest hypothesis. Throwaway is allowed.
- Phase 4 — Plan. With
requirements-quality,architecture-review,user-journey-mapper, anddecompose-work, turn surviving hypotheses into requirements, ADRs, journeys, epics, stories. - Phase 5 — Implement. Run each story through
tdd-workflowthenservice-implementation. No production code before the failing test and implementation contract exist. - Phase 6 — Harden. Route the build through the matching security skill (
security-owasp-web,security-mobile-masvs,security-owasp-llm, orsecurity-owasp-agentic) pluscve-zero-day-scannerbefore release. - Phase 7 — Ship. Use
qa-evalfor release readiness anddocumentation-graph-curatorto sync project memory and the Obsidian graph. - Phase 8 — Evaluate. Compare measured outcome against the original hypothesis kill criterion. Record what was proven, what was wrong, and what to learn next.
What ships with it
2 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 · 61 lines · 66 tokens per session scan A 1a180ad5d219
ai-pdlc is a skill published in the GitHub repository ashermahonin/agentic-skills (10 stars, last pushed 17d ago), licensed MIT. It adds 66 tokens to every session and 857 once invoked, about $0.0003 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 2026-08-31.
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