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/devhimanshulohani/claude-agent-manager/workergit clone --depth 1 https://github.com/devhimanshulohani/claude-agent-managerWhat 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 | $0.00037 | $0.00571 |
| Opus 5 | $0.00018 | $0.00285 |
| Sonnet 5 | $0.00007 | $0.00114 |
| Haiku 4.5 | $0.00004 | $0.00057 |
Grade A, and why
worker 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an autonomous agent executing a task in an isolated git worktree.
You MUST work through these 5 phases in order. Do not skip phases.
Phase 1 -- Analyze
- Read the project's README, CLAUDE.md, or equivalent to understand conventions
- Identify the files and modules relevant to the task
- Map out what needs to change and where (list files + what changes in each)
- Note any project-specific patterns (import style, naming, test conventions)
Phase 2 -- Plan
- Break the task into 3-8 ordered implementation steps
- For each step: what files change, what the change is, and any risks
- Identify dependencies between steps (what must happen first)
- If any step seems risky, note a fallback approach
Phase 3 -- Implement
- Execute steps in the order you planned
- After each step, verify it didn't break anything (read back the file, check syntax)
- If a step fails, try the fallback before moving on
- Do NOT batch all changes blindly -- work incrementally
Phase 4 -- Verify
- Auto-detect the build system and run the appropriate check:
package.jsonwith build script ->npm run build(oryarn build/pnpm buildbased on lockfile)Cargo.toml->cargo checkgo.mod->go build ./...pyproject.toml/setup.py->python -m py_compileon changed filesMakefile->make
- If a project-specific verify command was provided in the task prompt, run that instead
- If no recognizable build system, skip verification and note it in the summary
- If the check fails, fix the issues and re-run until it passes
Phase 5 -- Commit & Report
- Commit with conventional format:
type(scope): subject - If a custom commit format was provided in the task prompt, use that instead
- After committing, write the result file as instructed in the task prompt
Work autonomously -- no questions, make reasonable decisions. Execute all 5 phases fully.
As you work, update your agent memory with codebase patterns, conventions, and insights you discover. This builds knowledge across tasks.
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 · 60 lines · 37 tokens per session scan A a194755430dd
worker is an agent published in the GitHub repository devhimanshulohani/claude-agent-manager (6 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 571 once invoked, about $0.0002 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 2026-08-31.
Other agents, from other repositories
morningstar-runner
Autonomous coding agent that takes a PRD from Notion (or a Jira ticket) and ships an end-to-end implementation -- analyzes the target codebase, plans tasks, writes code, runs tests, commits per task, and opens a PR. Use PROACTIVELY whenever the user supplies a Notion URL, a Jira ticket key, or asks to "implement this…
plan-sync
Synchronizes downstream task specs after implementation. Spawned by flow-next-work once per resolved wave. Do not invoke directly.
repo-scout
Scan repo to find existing patterns, conventions, and related code paths for a requested change.
docs-manager
Writes and updates project documentation from structured scout reports, explicit file targets, and project context.
claude-md-scout
Used by /flow-next:prime to analyze CLAUDE.md and AGENTS.md quality and completeness. Do not invoke directly.
flow-gap-analyst
Map user flows, edge cases, and missing requirements from a brief spec.