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 HoangNguyen0403/agent-skills-standard --skill plan-featuregit clone --depth 1 https://github.com/HoangNguyen0403/agent-skills-standardWrote 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/hoangnguyen0403/agent-skills-standard/plan-feature)<a href="https://agentmods.dev/skills/hoangnguyen0403/agent-skills-standard/plan-feature"><img src="https://agentmods.dev/badge/skills/hoangnguyen0403/agent-skills-standard/plan-feature/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/hoangnguyen0403/agent-skills-standard/plan-feature"><img src="https://agentmods.dev/badge/skills/hoangnguyen0403/agent-skills-standard/plan-feature.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector 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 | $0.00029 | $0.01580 |
| Opus 5.5 | $0.00012 | $0.00632 |
| Sonnet 5.5 | $0.00006 | $0.00316 |
| Haiku 4.5 | $0.00003 | $0.00158 |
Grade A, and why
plan-feature 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 today.
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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Feature Skill
[!IMPORTANT] Plan a feature from BRD-lite brief or clear intent into PRD (What), decisions, implementation plan, and task slices.
Optional args: slug=, ticket=<id/url>, mode=interactive|autonomous|channel, channel=, auto_continue=true|false, profile=business|hybrid|technical.
Instructions
When the user asks to perform this workflow, execute the following steps:
Feature Planning Workflow (PRD / What)
Goal: Produce a PM-owned decision-complete PRD, delivery plan, and IT Department handoff before code starts.
Steps
- Load context:
- Load baseline PRD section, task-linked evidence, and canonical ledger (
docs/product/<slug>/evidence.json) with stable slug; when intent is unclear, ask which matching ticket/brief is authoritative rather than selecting an unrelated recent document; link strategy, OPP, DEC, critical assumption, and MET refs without duplicating records or minting product IDs as requirement IDs. - Explicitly load
product-management/product-requirementsand its on-demandreferences/requirements-modeling.md; report truthfully if unavailable. - Load
product-management/product-business-requirementswhen BRD-lite context applies; also load ticket text, existing specs, repo patterns,common-operator-profile,common-decision-discipline,quality-engineering-business-analysis, and matched framework skills. - Carry forward
operator_profile,lane,snc_tier,approval, BRD objective IDs, SMART metric, scope fence, assumptions, evidence ledger, glossary, risks, delivery context. Equivalent approved evidence suffices without separate BRD/PRD/SRS filenames; reused approved context skips rediscovery. - When a PRD relies on captured stakeholder meaning, load the applicable BRD confirmation procedure at
product-management/product-business-requirements/references/analysis-planning-and-elicitation.md; userequirements-modeling.mdas the sole owner of conflict, quality-versus-need, and transition semantics.
- Load baseline PRD section, task-linked evidence, and canonical ledger (
- Interview:
- Draft provisional PRD direction from current context before asking; ask only for business logic, scope, constraints, and acceptance criteria that cannot be inferred.
- Ask max 3 blocking decisions at a time with recommended default and 2-3 options; treat non-critical unknowns as explicit assumptions.
- Confirm problem statement, assumptions, target users, JTBD/use cases, platforms, data, analytics, security, performance, rollout, and non-goals.
- Confirm each requirement has owner, priority, and status; confirm success metrics and guardrails that must not regress.
- Carry legitimate no-build, defer, buy, substitute options, negative evidence, and permission boundaries; brief approval alone confers NO budget, exposure, pilot, or rollout consent.
- Stop when requirements are actionable.
- Draft PRD:
- Save to
docs/prd/prd-[slug].mdwhen file writes are allowed. - Keep "what" separate from "how"; consequential architecture/contracts route to
design-solution. - Add stable
REQ-*andAC-*IDs; link each requirement back to BRD-lite objective. One observable behavior per AC with necessary preconditions; truth tables for interacting conditions. - For substantial scope, build capability inventory (verb-object, in-scope/deferred/excluded); one containment parent per node, shared rules referenced without duplicate definitions.
- Use Given/When/Then AC when behavior can be misread; check user stories for persona, value, INVEST, happy/edge/negative paths.
- Select views by question (process flow=order, decision table=rules, state=lifecycle) per
requirements-modeling.md; prose/tables allowed, no mandatory UML. - Include risk categories, rollout/ops, decision log, analytics/telemetry, changelog. Mark unresolved blocking product decisions as blockers (name slice, consequence, owner, input).
- Include RACI table for BA, PM, architect, backend, frontend, mobile, QA, release, and business/UAT approver when more than one delivery role is involved.
- Save to
- Create implementation plan:
- Define product outcomes, ACs, dependencies, delivery risks, and provisional owner-aligned slices.
- Keep unresolved architecture/contracts explicit for
design-solution; do not author technical design twice in PM plan. - Map each provisional task slice to requirement IDs, AC IDs, likely owner role, repo/module, expected artifact, and verification lane.
- Estimate each slice (t-shirt size + confidence); roll up a delivery-window range in time/cost terms for
operator_profile=business, points otherwise. Identify whetherdesign-solutionis required before coding.
- Route:
- Continue when assumptions are non-critical; return BLOCKED for missing owner, untestable AC, approval, or release constraint.
- Empirical hypothesis testing needed ->
validate-product(when selected/installed; else record capability gap, keep hypothesis as unvalidated assumption, and route toimplementation-readiness); architecture/contracts unclear ->design-solution; approved plan with trace and testable ACs ->implementation-readiness.
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.
- today Changed · +1 lines 1a4d594d1da0
- 12d ago Changed · -4 lines 095beb6de6de
- 13d ago Changed f3cfa124f668
- 19d ago Changed 3e990125d0b8
- 28d ago Changed · +1 lines 8d249c39fe6d
- 1mo ago Changed · -2 lines 9c5bdcb50a99
- 1mo ago First seen · 99 lines · 29 tokens per session scan A dca7fcf31bd9
plan-feature is a skill published in the GitHub repository HoangNguyen0403/agent-skills-standard (582 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 1,580 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 2026-09-03.
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