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 agentmods add skills/dcosson/h2/plan-work-completion-signoffnpx skills add dcosson/h2 --skill plan-work-completion-signoffgit clone --depth 1 https://github.com/dcosson/h2Wrote 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/dcosson/h2/plan-work-completion-signoff)<a href="https://agentmods.dev/skills/dcosson/h2/plan-work-completion-signoff"><img src="https://agentmods.dev/badge/skills/dcosson/h2/plan-work-completion-signoff.svg" alt="Measured on agentmods" 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 | $0.00047 | $0.04475 |
| Opus 5 | $0.00023 | $0.02237 |
| Sonnet 5 | $0.00009 | $0.00895 |
| Haiku 4.5 | $0.00005 | $0.00447 |
Grade A, and why
plan-work-completion-signoff 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 6d 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 — 320 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Work Completion Signoff
Verify that implemented plan docs match their actual code. For each plan doc, an agent compares every specified feature, API, type, data structure, and test category against the real implementation. Complete docs get a signoff section appended; docs with gaps generate a report so the orchestrator can create follow-up beads.
This skill is a structured decision framework for the scheduler/concierge agent — it orchestrates multi-agent verification work. Individual agents do the actual comparison and signoff.
Inputs
$0(optional): Plans directory (default:docs/plans/)$1(optional): Code base path (default: repo root)
Phase 1: Discover Implemented Plans
- Read the plan index (
docs/plans/00-plan-index.mdor equivalent) - Identify which plan docs have been implemented — look for:
- Closed implementation beads/epics referencing those plans
- Existing code packages that correspond to plan components
- Plan doc status markers (e.g., "Implementation complete" in the index)
- Build a list of plan doc pairs to verify: each plan doc + its companion test harness doc (if exists)
- Exclude docs that already have a
## Completion Signoffsection (already verified in a prior pass)
Decision: Communicate the discovered doc list to the user or concierge for confirmation before proceeding. If the list looks wrong (too many or too few docs), clarify before creating beads.
Phase 2: Create Beads and Assign Agents
- Create an epic bead:
bd create "Plan completion signoff" --type epic --labels project={project} - Group plan docs into tasks — aim for 2-4 docs per task, grouped by component area:
- Group a plan doc with its companion test harness doc in the same task
- Related components can share a task (e.g., a storage layer plan + its test harness plan)
- Don't make tasks too small (one doc each) or too large (8+ docs)
- Create task beads under the epic, one per group
- Assign tasks to available agents. Prefer agents who:
- Wrote the implementation (they know the code best)
- Reviewed the implementation (they know the gaps)
- If original agents are unavailable, any agent can do it — the plan docs and code are self-documenting
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.
- 6d ago First seen · 320 lines · 47 tokens per session scan A fb7f184f97c8
plan-work-completion-signoff is a skill published in the GitHub repository dcosson/h2 (159 stars, last pushed 10d ago), licensed MIT. It adds 47 tokens to every session and 4,475 once invoked, about $0.0002 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 2026-08-30.
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