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.
git clone --depth 1 https://github.com/Langerrr/zforgeWrote 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/commands/langerrr/zforge/feature-resume)<a href="https://agentmods.dev/commands/langerrr/zforge/feature-resume"><img src="https://agentmods.dev/badge/commands/langerrr/zforge/feature-resume/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/commands/langerrr/zforge/feature-resume"><img src="https://agentmods.dev/badge/commands/langerrr/zforge/feature-resume.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 | $0.00009 | $0.01100 |
| Opus 5.5 | $0.00004 | $0.00440 |
| Sonnet 5.5 | $0.00002 | $0.00220 |
| Haiku 4.5 | $0.00001 | $0.00110 |
Grade A, and why
feature-resume 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/feature-resume — Interactive Feature Implementation
Continue a feature plan with the planner implementing in-session and checking in with the user between phases.
Load the feature-execution skill. It owns phase state, recovery, and acceptance. This command supplies the interactive intent only.
Arguments
$1: Feature name (snake_case, or will be converted)
Pre-flight
Same as /feature-orchestrate: resolve the feature directory — below docs/ on a unique match where the default path is absent — read the overview, context, plan, Doc Map, decision ledger and session log, append this session's row, and note inherited standing flags. Implementation is this session, so the model question is for acceptance only: where it will be delegated, ask which model accepts, proposing the previous row's choice or a cheaper model than this session's, and record it in the row.
Then scan the phase files and report the current state to the user before doing anything: which phases are complete, which is next, what is REPORTED, PAUSED or INTERRUPTED, and which standing flags are open.
Order of work
- REPORTED — a previous session took a report and never verified it. Run acceptance before anything else; do not resume that phase's agent and do not trust the report.
- PAUSED — answer the question first. Present it with enough context for the user to decide, or resolve it from the plan and say that you did. A
REASON: USAGE_LIMIT_95pause has no question in it — pick up from the phase's## Resume Point. - INTERRUPTED — resume per the skill's recovery procedure. Re-orient against disk before continuing; the phase may be further along than the checklist claims.
- FAILED — present the error and ask whether to fix, skip, or stop.
- READY — start the next phase in order.
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 Changed 27448e7557d6
- 1mo ago Changed 3855bd3d4dd7
- 2mo ago First seen · 64 lines · 9 tokens per session scan A 1d1ffba9bc36
feature-resume is a command published in the GitHub repository Langerrr/zforge (12 stars, last pushed 1mo ago), licensed MIT. It adds 9 tokens to every session and 1,100 once invoked, about $0.0000 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.
Other commands, from other repositories
issue-plan-review
Read a GitHub issue and all comments, review the implementation plan, independently assess each finding, and present findings.
pr-review
Run a comprehensive PR review, post results, then independently assess each finding.
issue-assessment
Read a GitHub issue, perform an independent assessment, and present findings.
pr-pre-merge
Run pre-merge checklist — branch freshness, docs, version, CHANGELOG, tests.
pr-review-fix
Fix specific issues from a PR review using feature-dev.
simplify
The over-engineering review: five tags (delete, stdlib, native, yagni, shrink), a mandatory replacement per finding, and a real null result when there is nothing to cut.