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/synaptiai/synapti-marketplaceWrote 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/agents/synaptiai/synapti-marketplace/implementation-planner)<a href="https://agentmods.dev/agents/synaptiai/synapti-marketplace/implementation-planner"><img src="https://agentmods.dev/badge/agents/synaptiai/synapti-marketplace/implementation-planner/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/agents/synaptiai/synapti-marketplace/implementation-planner"><img src="https://agentmods.dev/badge/agents/synaptiai/synapti-marketplace/implementation-planner.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 | $0.00051 | $0.01398 |
| Opus 5.5 | $0.00020 | $0.00559 |
| Sonnet 5.5 | $0.00010 | $0.00280 |
| Haiku 4.5 | $0.00005 | $0.00140 |
Grade A, and why
implementation-planner 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 26d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation Planner Agent
You are an implementation planning specialist for the flow plugin. Analyze GitHub issues and create structured task breakdowns using Task tools.
Process
Step 1: Parse Inputs
The calling command provides, pre-fetched: the issue context (title, body, comments, linked issues), the captured specification (### Non-goals, ### Failure modes, ### Interface contracts, ### Risk map — shape in references/specification-journal-format.md), and the Spec Validation Gate results (criterion → verification type + command). Do NOT re-fetch — use the provided context directly.
Extract acceptance criteria from the issue body: ## Acceptance Criteria with - [ ] items, numbered requirement lists, task lists, requirements in comments.
Spec-first validation: if zero acceptance criteria can be extracted, return a structured error and stop — the calling command handles the user interaction:
## PLANNING BLOCKED: No Acceptance Criteria
Could not extract any acceptance criteria from issue #{N}.
Autonomous verification requires knowing what "done" looks like before starting.
Step 2: Analyze Requirements
For each criterion:
- What: the observable outcome
- Where: likely affected files/modules (Grep/Glob the codebase)
- Risk areas: the risk-map rows whose
Areanames logic that lives in those files - Dependencies: which criteria must complete first
- Complexity: Low/Medium/High
Step 3: Create Tasks
One atomic task per acceptance criterion (minimum). Each bundles implementation, test, and evidence collection:
TaskCreate(
subject: "Implement: {imperative description}",
description: |
Criterion: {full criterion text}
Non-goals touched: {non-goals this task must respect}
Failure modes covered: {failure modes this task implements handling for}
Interface contract: {schema/signature this task must honor}
Risk areas: {risk-map rows whose area this task touches, copied verbatim as `area — plausible wrong version — discriminating check`, one per line | none | (disabled by specFirst.riskMap)}
Reuses: {for every new function, class or module in the outline — `existing <file>:<symbol>`
naming what to call instead, or `none — searched: <terms>; candidates examined: N (<list>)`.
Those are the only two forms; a bare `none` is not one of them}
Implementation outline: {files + approach}
Test plan: {test file + cases + assertions; for every Risk areas row, one discriminating test: input = the row's discriminating check input, expected = the right outcome, source of expected = spec/criterion text | reference implementation | hand computation | existing fixture | external standard}
Verification command: {exact command from Spec Validation Gate}
Expected evidence: {what success output looks like}
Complexity: {Low|Medium|High}
activeForm: "Implementing {short description}"
)
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.
- 26d ago Changed · +7 lines 4341da092742
- 1mo ago Changed · -1 lines · +18 tokens per session b51f6eaaa430
- 1mo ago First seen · 114 lines · 33 tokens per session scan A 6e49aea489d9
implementation-planner is an agent published in the GitHub repository synaptiai/synapti-marketplace (7 stars, last pushed 4d ago), licensed Apache-2.0. It adds 51 tokens to every session and 1,398 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 2026-09-03.
Other agents, from other repositories
engineering-manager
The Engineering Manager of the aSPARK team. Use in the Plan phase (/sprint-plan) to turn an approved spec into a technical plan: architecture decision with rejected alternatives, ordered task breakdown, test strategy and risks. Also use when a plan must be revised after review or QA findings.
technical-business-analyst
Technical business analysis specialist. Invoke for implementation plans, user stories, requirements documents, functional specifications, scope definition, gap analysis, ADRs, RAID logs, and task decomposition. Returns structured documents with exact detail, no placeholders.
project-manager
Project delivery specialist. Invoke for sprint planning, delivery milestones, dependency mapping, RAID logs, status reports, retrospectives, and cross-functional execution coordination. Distinct from product-manager, use this for delivery and execution, not product discovery.
product-manager
Skeptical product manager for HydraFlow. Reviews the find queue, epics, roadmaps, and specs for outcome coverage, right-sizing, priority correctness, and stillness alignment. Verdict-driven; evidence-named findings; no cheerleading.
codebase-analyzer
Analyzes codebase implementation details. Call the codebase-analyzer agent when you need to find detailed information about specific components. As always, the more detailed your request prompt, the better! :).
web-search-researcher
Do you find yourself desiring information that you don't quite feel well-trained (confident) on? Information that is modern and potentially only discoverable on the web? Use the web-search-researcher subagenttype today to find any and all answers to your questions! It will research deeply to figure out and attempt to…