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 djscheuf/agentic-dev-ecosystem-template --skill edd-plangit clone --depth 1 https://github.com/djscheuf/agentic-dev-ecosystem-templateWrote 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/djscheuf/agentic-dev-ecosystem-template/edd-plan)<a href="https://agentmods.dev/skills/djscheuf/agentic-dev-ecosystem-template/edd-plan"><img src="https://agentmods.dev/badge/skills/djscheuf/agentic-dev-ecosystem-template/edd-plan/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/djscheuf/agentic-dev-ecosystem-template/edd-plan"><img src="https://agentmods.dev/badge/skills/djscheuf/agentic-dev-ecosystem-template/edd-plan.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 | $0.00060 | $0.01524 |
| Opus 5.5 | $0.00024 | $0.00610 |
| Sonnet 5.5 | $0.00012 | $0.00305 |
| Haiku 4.5 | $0.00006 | $0.00152 |
Grade A, and why
edd-plan 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 22d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Steps:
Read-only planning. Do not run shell commands or re-run evaluations — the workflow supplies all evaluation evidence as input documents. Plan strictly from the files you can read.
1. Read the Run's Process Directory
- Read the EDD run folder provided as input, e.g.
.process/edd/<run_id>/. - Read
progress.jsonfor remaining iteration/token budgets,best_accepted_state,consecutive_confirmed_regressions, andcandidate_history. - Read the running
refinement.yamldocument. It embeds the originaledd_inputobject, which supplies the target skill's rubric/prompt text, the required-test-case catalog, the baseline metrics, the per-test-case coverage map, and the authorized modification scope. - Also read the highest-numbered
iterations/<n>/{plan,do,check}.jsonfiles for the current iteration state.
2. Review the Latest Evaluation Evidence
- Read the two most recent
check.jsonresults — they are supplied directly as additional input paths in the invocation prompt (or the single baseline result if this is the first iteration). - You MUST read the json output DIRECTLY. You are Forbidden from using any commands to read the file.
- Identify common failure patterns: are failures concentrated in one rubric dimension, one fixture, or one assertion helper?
- Cross-reference the required-test-case coverage map in the latest
check.jsonagainst the target skill's_tests/*.tests.yamland required-test-case catalog to find any required case with no covering assertion.
3. Review Prior Plan/Do/Check History
- Read every prior iteration's
plan.json,do.json, and its outcome (accepted/rejected/reverted) fromcandidate_historyandrefinement.yaml. - Do not propose an action that repeats a change already confirmed as a regression and reverted, unless the new plan explicitly states what will be different this time and why it is expected to avoid the prior failure.
4. Select the Next Action
Choose exactly one action from this closed taxonomy:
What ships with it
5 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.
- 22d ago First seen · 73 lines · 60 tokens per session scan A a31d2b65de5c
edd-plan is a skill published in the GitHub repository djscheuf/agentic-dev-ecosystem-template (12 stars, last pushed 20d ago), licensed MIT. It adds 60 tokens to every session and 1,524 once invoked, about $0.0002 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 2026-10-01.
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