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-dogit 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-do)<a href="https://agentmods.dev/skills/djscheuf/agentic-dev-ecosystem-template/edd-do"><img src="https://agentmods.dev/badge/skills/djscheuf/agentic-dev-ecosystem-template/edd-do/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-do"><img src="https://agentmods.dev/badge/skills/djscheuf/agentic-dev-ecosystem-template/edd-do.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 | $0.00048 | $0.01254 |
| Opus 5.5 | $0.00019 | $0.00502 |
| Sonnet 5.5 | $0.00010 | $0.00251 |
| Haiku 4.5 | $0.00005 | $0.00125 |
Grade A, and why
edd-do 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EDD-Do
Purpose
Apply exactly one planned refinement action to a target skill and/or its evaluation suite. Make only the change described in the plan.
No shell commands. Do not run shell commands (exec, git, npm, etc.) — every needed check is either a file edit or a re-read. The workflow runs validation (scope checks, the full evaluation) deterministically after this skill completes.
Prerequisites
iterations/<n>/plan.jsonexists and names an authorized action,intended_files, andexpected_effect.- The target repository worktree has no stray edits from a previous, not-yet-committed-or-reverted attempt.
- If
actionispropose_evaluation_expectation_change, approval has already been recorded for this exact proposal. If it has not, STOP and report this to chat instead of proceeding — never apply an unapproved expectation change.
Workflow Steps
Step 1: Read the Plan
Read iterations/<n>/plan.json:
- action
- intended_files
- rationale
- expected_effect
- iteration_start_baseline
Read refinement.yaml's latest section for full narrative context and any
"changes made" notes left by a prior, related iteration.
Step 2: Load Skill and Eval Context
Read the target skill's SKILL.md / prompt and any supporting documents
named in intended_files.
If the change touches promptfooconfig.yaml, test YAML, custom
assertion/transform JS, or fixtures:
→ Consult the `promptfoo` skill FIRST.
→ Use its phase map to jump to the specific section needed
instead of guessing at syntax or option names.
Step 3: Make the Minimal Change
Modify ONLY the files listed in intended_files.
By action type:
- refine_skill:
Edit only the prompt/instruction sections implicated by the
failure evidence in the plan. Do not rewrite unrelated sections.
- repair:
Fix the fixture/assertion/helper defect without lowering the bar
the evaluation is meant to enforce. Prefer fixing an internally
inconsistent fixture over relaxing an assertion. If the true cause
is a helper bug (e.g. `||` treating `0` as falsy), fix the helper,
not the test data.
- add_coverage:
Add the missing required test case(s). Prefer an existing,
already-rich fixture over authoring a new one; follow the target
skill's own fixture-naming and YAML-block conventions (copy an
existing block, change only what differs).
- refine_supporting_docs:
Edit only the referenced/example/template document(s) named in
intended_files.
- propose_evaluation_expectation_change:
Change only the approved expectation, exactly as approved. Adhere to the approved diff, other changes are forbidden
- Editing a file not listed in intended_files IS FORBIDDEN
- Refactoring, renaming, or "improving" anything the plan did not ask for IS FORBIDDEN
- Weakening an expectation to make a failure disappear instead of
fixing the underlying fixture, helper, or skill behavior IS FORBIDDEN
What ships with it
4 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 · 133 lines · 48 tokens per session scan A 6e3e825d0c2f
edd-do is a skill published in the GitHub repository djscheuf/agentic-dev-ecosystem-template (12 stars, last pushed 20d ago), licensed MIT. It adds 48 tokens to every session and 1,254 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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