Metagit Agent Access

Metagit agent access optimizer

On-demand workflow to make a target repository easy for AI agents to grasp with minimal tokens, using conventions that stay out of the human reading path where possible.

When to use

• User asks to optimize agent access, agent onboarding, or llms.txt
• User wants hidden agent metadata in an existing project
• After adding major CLI/MCP features to a product repo (refresh agent surfaces)

Do not run proactively on every task.

Execution modes

1) Script (run first)

Hermes skill_manage installs SKILL.md only — local ./scripts/ paths will not exist. Prefer the PyPI package path (always available when metagit-cli is installed) or metagit skills install --skill metagit-agent-access for a full skill tree.

From the target repository root (not necessarily metagit-cli):

SKILL_ROOT="$(python3 -c "import metagit, pathlib; print(pathlib.Path(metagit.__file__).parent / 'data/skills/metagit-agent-access')")"
"$SKILL_ROOT/scripts/optimize-agent-access.sh" . --apply --json

After metagit skills install --skill metagit-agent-access:

"${HERMES_HOME:-$HOME/.hermes}/skills/metagit-agent-access/scripts/optimize-agent-access.sh" . --apply --json

Inline fallback (no scripts)

Run the optimizer Python entrypoint from the installed package:

SKILL_ROOT="$(python3 -c "import metagit, pathlib; print(pathlib.Path(metagit.__file__).parent / 'data/skills/metagit-agent-access')")"
python3 "$SKILL_ROOT/scripts/optimize_agent_access.py" . --json
python3 "$SKILL_ROOT/scripts/optimize_agent_access.py" . --apply --json

If uv is available in the target repo, the shell wrapper is equivalent:

uv run python "$SKILL_ROOT/scripts/optimize_agent_access.py" . --json

Manual audit when scripts cannot run: check for llms.txt, AGENTS.md, docs/agents.md, and <!-- agent-access:start --> in README; scaffold from $SKILL_ROOT/templates/ only after dry-run review.

Flags:

• --dry-run (default): report gaps only
• --apply: create missing scaffolds from templates; inject README HTML comment if absent
• --json: machine-readable report on stdout

2) Subagent (on demand)

When the script report shows gaps the templates cannot fill, or the repo is non-trivial, dispatch a generalPurpose subagent (not background) with the prompt in subagent-prompt.md.

Replace {{REPO_ROOT}} with the absolute target path. The subagent must:

1. Run the optimizer script with --json
2. Apply --apply if safe (no overwrite of human-authored agent sections without diff review)
3. Add/refine hidden metadata (HTML comments) before visible prose changes
4. Keep human-facing README diff minimal (one short pointer table max)
5. Return a JSON summary: files_created, files_updated, token_estimate_llms_txt, follow_ups

Parent agent: merge subagent summary; do not paste full generated files into chat.

Artifacts (priority order)

See reference.md for formats.

Quality bar

• Token budget: llms.txt ≤ 80 lines; AGENTS.md agent section ≤ 120 lines before project-specific content
• No duplication: llms.txt indexes; AGENTS.md quick reference; docs/agents.md full guide
• Session block: every artifact set includes install + 2–3 session-start commands
• Idempotent: re-run --apply must not duplicate HTML comment blocks or sections

Metagit-specific extras

If target has .metagit.yml, include in generated session block:

export METAGIT_AGENT_MODE=true
metagit context pack --tier 2 --json
metagit skills install --scope user

Verification

After --apply:

test -f llms.txt && test -f AGENTS.md
rg 'agent-access:start' README.md  # hidden block present

If mkdocs.yml exists, add agents.md to nav and run mkdocs build --strict when docs CI expects it.

Additional resources

• reference.md — hidden metadata patterns
• subagent-prompt.md — full subagent dispatch text
• templates/ — scaffold files