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 encoreshao/encore-skills --skill pm-workflowgit clone --depth 1 https://github.com/encoreshao/encore-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/encoreshao/encore-skills/pm-workflow)<a href="https://agentmods.dev/skills/encoreshao/encore-skills/pm-workflow"><img src="https://agentmods.dev/badge/skills/encoreshao/encore-skills/pm-workflow/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/encoreshao/encore-skills/pm-workflow"><img src="https://agentmods.dev/badge/skills/encoreshao/encore-skills/pm-workflow.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 | $0.00030 | $0.01420 |
| Opus 5.5 | $0.00012 | $0.00568 |
| Sonnet 5.5 | $0.00006 | $0.00284 |
| Haiku 4.5 | $0.00003 | $0.00142 |
Grade A, and why
pm-workflow 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 1mo 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PM Workflow
The PM loop is not about code — it's about getting the problem definition right before anyone writes a line. A vague issue creates vague work. Your job is done when an engineer can pick this up and start without asking you a single question.
The loop
write-issue → share → gather-feedback → synthesize → refine → validate → finalize
↑ |
└───────── iterate ────┘
Entry points
| Where you are | Start here |
|---|---|
| Rough idea, bug report, user complaint | Phase 1: Draft |
| Issue drafted, not yet shared | Phase 2: Share |
| Feedback collected, need to update issue | Phase 4: Synthesize |
| Issue refined, checking if it's ready | Phase 5: Validate |
Phase 1: Draft the issue
Use write-issue. Don't try to be complete — a good draft is specific about the problem and honest about what's still unknown.
Gate: The issue has a clear problem statement, a best-guess root cause, and at least draft acceptance criteria.
Phase 2: Share with stakeholders
Post the issue link and ask for structured feedback — not just "does this look right?"
# Post to GitLab and get the URL
GITLAB="$HOME/.claude/skills/gitlab-config/scripts/gitlab_api.py"
python $GITLAB get-issue <project> <issue_iid> # confirm it's visible
# Or create it first if still a draft
glab issue create --title "TITLE" --description "DESCRIPTION"
Who to share with:
- Users affected — do they recognize this as their problem?
- Stakeholders — is this the right priority? any constraints?
- Designer (if applicable) — any UX implications missed?
- Tech lead — is the proposed approach feasible? any hidden complexity?
Ask specific questions, not open ones:
- "Does this match what you reported in [original report]?"
- "Is acceptance criterion #2 testable from your side?"
- "What would make this out of scope?"
Phase 3: Gather feedback
Collect responses. Document them directly — don't rely on memory.
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.
- 1mo ago First seen · 173 lines · 30 tokens per session scan A 13e62bc58bdc
pm-workflow is a skill published in the GitHub repository encoreshao/encore-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 1,420 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 2026-08-31.
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