Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/d-o-hub/github-template-ai-agentsnpx agentmods add skills/d-o-hub/github-template-ai-agents/implementerWrote 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/d-o-hub/github-template-ai-agents/implementer)<a href="https://agentmods.dev/skills/d-o-hub/github-template-ai-agents/implementer"><img src="https://agentmods.dev/badge/skills/d-o-hub/github-template-ai-agents/implementer/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/d-o-hub/github-template-ai-agents/implementer"><img src="https://agentmods.dev/badge/skills/d-o-hub/github-template-ai-agents/implementer.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 | $0.00688 |
| Opus 5.5 | $0.00024 | $0.00275 |
| Sonnet 5.5 | $0.00012 | $0.00138 |
| Haiku 4.5 | $0.00006 | $0.00069 |
Grade A, and why
implementer 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementer Skill
The Implementer skill is an execution-focused tier responsible for making atomic, high-quality code changes.
When to Use
- Implementing features based on a solid technical specification
- Applying identified bug fixes
- Executing planned refactoring tasks
- Ensuring all changes pass quality gates and tests
- Even if they just say "implement this" or "make the changes"
Blueprint Validation
Before making any changes, verify the Blueprint (ADR) exists and includes:
- Clear problem statement
- Proposed solution with specific files to modify
- TRIZ contradiction analysis (if applicable)
- Success criteria and test plan
- Rollback strategy
If the Blueprint is missing or incomplete, stop and request it before proceeding.
Workflow
- Blueprint Check: Verify that a clear implementation strategy (ADR) with TRIZ analysis exists.
- Atomic Edits: Apply changes one concern at a time. Each edit should be independently committable.
- Verification: Run tests and the quality gate after each atomic change.
Atomic Edit Pattern
1. Identify the single concern to change
2. Make the edit
3. Run: ./scripts/quality_gate.sh
4. If pass → commit with conventional format
5. If fail → fix, re-run quality gate, then commit
6. Move to next concern
Gotchas
- Never bundle unrelated changes in a single commit — if the quality gate fails, you can't tell which change caused it.
- If the Blueprint says "modify files X, Y, Z" but you find a better approach mid-implementation, update the Blueprint first rather than deviating silently.
- Run the quality gate locally before pushing — CI failures are expensive to diagnose remotely.
Rationalizations
| Rationalization | Reality |
|---|---|
| "I'll just fix it now" | Skipping the Blueprint phase leads to scope creep and architectural regressions. |
| "The change is obvious, no need for a Blueprint" | Obvious changes still need atomic edits and quality gate verification. |
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 · 81 lines · 60 tokens per session scan A 6fc85c6fab5e
implementer is a skill published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed yesterday), licensed MIT. It adds 60 tokens to every session and 688 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 2026-08-31.
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