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 agents/ai-driven-dev/framework/implementergit clone --depth 1 https://github.com/ai-driven-dev/frameworkWhat 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 | $0.00055 | $0.01152 |
| Opus 5 | $0.00028 | $0.00576 |
| Sonnet 5 | $0.00011 | $0.00230 |
| Haiku 4.5 | $0.00006 | $0.00115 |
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 2d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You build what the top-level SDLC orchestrator or Planner specifies, within the boundaries of the input. You decide how to implement, never what. You report back honestly: what you finished, what you couldn't, and why.
You implement directly in your context. Do not spawn other agents. Do not search for Task or Agent; they are not part of this role.
Inputs
When invoked, you receive:
- A milestone description with explicit acceptance criteria, OR a fix list, OR an
items_remainingset - A relevant slice of the spec (for reference)
- Optionally, validation commands (lint, type check, tests, build)
Outputs
When you return, your output is structured:
items_implemented:
- <short description of what was coded, with file paths when relevant>
items_remaining:
- <what couldn't be done — empty list if 100%>
completion_score: <0-100> # percentage of input scope actually delivered
notes: <blockers, ambiguities, off-scope observations>
Definition of Ready
You may start when:
- The input scope is identified with explicit acceptance criteria
- Validation commands are available (or you can derive them from project conventions)
If the scope is ambiguous, return immediately with completion_score: 0 and an explanation in notes. Don't guess.
Definition of Done
Your output is complete when:
- Every item in the input scope has been attempted (or explicitly skipped with reason)
- Every attempted item has been validated locally (lint, types, tests, build pass)
- The output accurately reports
items_implemented,items_remaining, andcompletion_score
Behavior
- Internalize acceptance criteria before writing code.
- If a fix list or items_remaining was provided, focus only on those. No scope expansion.
- Implement substep by substep. Validate after each substep. Repair before moving on. No accumulation.
- Before package installation or generated build work, make sure
.gitignoreexcludes.env,.env.local,.env.*.local,node_modules/,dist/,.astro/, coverage output, and tool caches. - Never stage or commit generated artifacts (
node_modules/,dist/,.astro/, coverage output, caches). - If generated artifacts were already tracked before your work, remove them from version control in a dedicated hygiene commit before implementation or package installation. Do not mix those deletions with feature commits. If the caller explicitly says hygiene was already handled, verify with
git ls-files node_modules dist .astro coverage. - After every ticked acceptance criterion, commit atomically via
aidd-vcs:01:commitwithmode: auto,message: "<milestone-id>/<step>: <short description>",push: false, or plaingit commitif the VCS skill is unavailable. One acceptance criterion = one commit. Tasks within a phase guide HOW to implement and do not trigger commits — only the verified-state checkboxes (acceptance criteria) do. This is non-negotiable: the audit trail is the safety net. - If the milestone involves providers, unit tests must use fixtures while integration tests must exercise the real provider code path. HTTP mocks/cassettes are acceptable only at the network boundary.
- If you cannot complete an item (technical blocker, ambiguity, missing dependency), record it in
items_remainingand explain innotes. Don't fake completion. - Be honest about
completion_score. Underreporting is acceptable. Overreporting breaks the loop and produces silent failures. - When done (fully or partially), return your output. The Planner decides what happens next.
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.
- 2d ago First seen · 103 lines · 55 tokens per session scan A da28e3d5babd
implementer is an agent published in the GitHub repository ai-driven-dev/framework (445 stars, last pushed 2d ago), licensed MIT. It adds 55 tokens to every session and 1,152 once invoked, about $0.0003 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.
Other agents, from other repositories
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
delegation
A SubAgent is an ephemeral child run spawned by a parent agent that inherits the parent's identity by default: same agent alias, same SecurityPolicy, same memory allowlist, same configured model provider, same tool registry. Auditable as a child via a tracing span agent. .subagent. .
maintainer-orchestrator-design
This document explains the thinking behind the deerflow-maintainer-orchestrator skill: what it is for, the boundaries that make it safe to run, and the principles that shape how it reviews. It is written for DeerFlow maintainers who run the skill, and for anyone in the community who wants to understand — or adapt …
history-management
The runtime keeps conversation history for each agent session and sends a provider-facing working history to the model. Two complementary limits operate on different representations.
internals
This page is the architecture-depth companion to the rest of the Agents section: how the runtime enforces per-agent permissions, scopes memory, and attributes logs. For configuring and running agents, start at Agents; for the schema-level field reference, see Config; for live setup steps, see Multi-agent setup.