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 tikalk/adlc-team-skills --skill product-initgit clone --depth 1 https://github.com/tikalk/adlc-team-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/tikalk/adlc-team-skills/product-init)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/product-init"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/product-init/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/tikalk/adlc-team-skills/product-init"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/product-init.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high Memory Poisoning · line 174 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- medium Excessive Agency · line 82 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 | $0.00025 | $0.02556 |
| Opus 5.5 | $0.00010 | $0.01022 |
| Sonnet 5.5 | $0.00005 | $0.00511 |
| Haiku 4.5 | $0.00003 | $0.00256 |
Grade A, and why
product-init 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 25d 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 — 329 lines — stays where its author put it; the contents beside it link to each section on GitHub.
product-init
What this skill does
Reverse-engineers product decisions from an existing product using a three-phase analysis pipeline:
- Discovery Agent: Scans each feature-area for raw product signals (code, docs, pricing)
- Pattern Agent: Classifies signals into PDR categories, scores strategic importance
- Synthesis Agent: Cross-feature-area analysis, flags inconsistencies
Output: Individual PDR-{NNN}.md files (status Discovered) in .adlc/drafts/pdr/ with an auto-generated pdr.md index.
When to use
- Existing product with no formal PDRs
- Brownfield codebase needs product documentation
- Team onboarding — walking through product rationale
- Post-acquisition or inherited codebase
When NOT to use
- New product (use
/product-specifyinstead) - Minor PDR updates (use
/product-clarifyinstead)
Execution Steps
Phase 0: Environment Setup
Run setup script to resolve paths and detect feature-areas:
sh: scripts/bash/setup-product-init.sh [--json]
ps: scripts/powershell/setup-product-init.ps1
Requires: the product-clarify skill (provides pdr-lib.sh): adlc-cli skills add tikalk/adlc-team-skills --skill product-clarify
Setup output (JSON):
{
"REPO_ROOT": "/path/to/project",
"PDR_DRAFTS_DIR": "/path/to/project/.adlc/drafts/pdr",
"PRD_FILE": "/path/to/project/PRD.md",
"feature_areas": ["core", "business", "growth"],
"next_pdr": "001"
}
Create directories:
mkdir -p "{REPO_ROOT}/.adlc/drafts/pdr"
mkdir -p "{REPO_ROOT}/.adlc/product"
Phase 1: Feature-Area Detection
Detect feature-areas from three sources:
| Source | Detection Pattern |
|---|---|
| Directory Structure | src/auth/, features/payments/, modules/ |
| Documentation | README sections, existing PRD, ROADMAP |
| Pricing Tiers | Starter/Pro/Enterprise feature mapping |
Present detected areas:
## Detected Feature-Areas
| # | Feature-Area | Sources | Evidence |
|---|--------------|---------|----------|
| 1 | **Core** | Directory + Docs | src/users/, README "Core Features" |
| 2 | **Business** | Directory + Pricing | src/billing/, pricing.md tiers |
Reply: Y to confirm, n for monolithic, or suggest changes.
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.
- 25d ago Changed 968dc6d09d1b
- 26d ago Changed · +2 lines 1f2f883a8573
- 1mo ago Changed b301bd7866fb
- 1mo ago Changed · -18 tokens per session fd3941294a08
- 1mo ago Changed · -8 lines 9a06afacc100
- 1mo ago First seen · 335 lines · 43 tokens per session scan A 2e853cec36c0
product-init is a skill published in the GitHub repository tikalk/adlc-team-skills (143 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 2,556 once invoked, about $0.0001 per session on Opus 5.5. 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.
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Use when a long-running agent may have lost the product outcome, when context was compacted, when work has crossed a major boundary, or when a fresh path-based trace is needed before the next consequential action. Not for fresh-session orientation, waking an idle seat, or checkpoints.