agentify

A beta setup tool that adds an automated feedback process to a GitHub repository. GitHub is a service for hosting code and reviewing changes.

In plain words
What is it for?
For installing workflows that receive feedback, create triaged GitHub issues, prepare fix pull requests, notify the person who reported the issue, and handle release callbacks.
Why use it?
It organizes incoming feedback into issues and prepares fixes through a human review step before changes are merged.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/tokencanopy/e2a/agentify
Any agent
npx skills add tokencanopy/e2a --skill agentify
Clone the repo
git clone --depth 1 https://github.com/tokencanopy/e2a

Made for: Claude Code, Codex.

Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,462 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00093 $0.01462
Opus 5 $0.00046 $0.00731
Sonnet 5 $0.00019 $0.00292
Haiku 4.5 $0.00009 $0.00146

Measured yesterday against content hash 18bbbefb22f1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agentify 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 yesterday.

The scan reads SKILL.md. This mod also ships 22 executable files (agentify-render.sh, examples/e2a/agentify-fix-verify-setup.sh, safe-paths.sh, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/e2a-labs/skills/agentify/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

agentify — deploy the autonomous-repo feedback loop

Beta. This skill is under active development; the setup flow may change and a lower-friction "express" onboarding is being explored.

/agentify installs the framework into a target repo: feedback in → triaged GitHub issue → human-gated fix PR → filer notified. It bundles the framework as templates, so installing this plugin is grabbing the framework. The install lands as a PR the repo owner reviews and merges — the install itself goes through the same human gate the framework runs on.

Where the tooling lives. The scaffolder and templates ship in this skill's own directory, next to this SKILL.mdagentify-render.sh, templates/, references/. Substitute that absolute path for $AGENTIFY_DIR below; the scaffolder resolves its own resources, so any working directory works.

v0 scope. The full loop ships: triage/intake (email → triaged issue), comms (filer acks + the fix-gate approval email + verified-reply routing), fix + release (the coding agent's human-reviewed PR + the merge→shipped callback), and this deploy flow. The mechanical render is automated (agentify-render.sh, with a _selftest); the Q&A and the one-time identity/secret setup stay guided. An update mode (re-render preserving the adopter's config tweaks) is the natural follow-on — the render is already idempotent.

What gets scaffolded into the target repo

from $AGENTIFY_DIR/templates/ to the target repo
autonomous-repo.config.yml.tmpl autonomous-repo.config.yml (the only file the adopter owns)
runtime-skill/** .claude/skills/autonomous-repo/**
scripts/*.sh (ticket_card.sh, comms_send.sh, released_markers.sh) scripts/*.sh
workflows/*.yml.tmpl .github/workflows/*.yml

Deploy procedure

  1. Detect. Read the target repo: its OWNER/REPO, primary language, test command, and CI — used to fill the fix-lane verify_setup_script and sensible defaults.
  2. Configure. Ask the adopter the config values and export them as the ANS_* env vars agentify-render.sh reads: ANS_PRODUCT_NAME, ANS_OWNER, ANS_REPO, ANS_MARKER, ANS_REVIEWER_LOGIN, ANS_BOT_LOGIN, ANS_SUPPORT_ADDRESS, ANS_FIX_GATE_MODE (hitl recommended), ANS_APPROVER_ADDRESS, ANS_VERIFY_SETUP_SCRIPT. (The bot login can be filled later from the checklist; secrets are never gathered here.)
  3. Render. Run "$AGENTIFY_DIR/agentify-render.sh" --to <target-repo-root>. It fills autonomous-repo.config.yml from the ANS_* answers (failing loudly on any unfilled placeholder) and scaffolds the runtime skill, the scripts, and the four workflows into their real paths (.claude/skills/autonomous-repo/, scripts/, .github/workflows/*.yml). Re-running updates the scaffolded code but PRESERVES an existing autonomous-repo.config.yml (your tuned always_hitl, the filled bot_login) — pass --force only to regenerate the config. Then tune the rendered config's always_hitl list for the product's sensitive surfaces, and sanity-check: scripts/*.sh _selftest all green and the config parses. Optional addons ($AGENTIFY_DIR/templates/addons/) — e.g. submit-feedback-mcp (a submit_feedback MCP tool that email-bridges into the support mailbox) — are opted in via ANS_ADDONS="<name> ..."; the render scaffolds each to tools/<name>/ and appends its setup to AGENTIFY-ADDON-SETUP.md. Addons are additive; the loop runs without them.
  4. Auto-do the safe parts. Create the labels from labels.* via gh (feedback, agent-fix, wontfix, feedback-ops, the status:* set).
  5. Hand off the rest (print, don't do — see references/setup-checklist.md): create the GitHub App (bot identity) and set github_app_login in config; create the e2a support@ agent + an agent-scoped API key; add the repo secrets (CLAUDE_CODE_OAUTH_TOKEN or ANTHROPIC_API_KEY, E2A_API_KEY, AUTOREPO_APP_ID, AUTOREPO_APP_PRIVATE_KEY); enable Actions; set branch protection so the fix lane's PRs require review. Hand over the exact commands/links — never run the auth yourself.
  6. Open the install as a PR. Branch, commit the scaffolded files, open a PR titled "agentify: install the autonomous-repo feedback loop" that summarizes what each file does and links the setup checklist. Do not merge.

Read the full file on GitHub · 100 lines

Files

What ships with it

56 files 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.

Changes

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.

  1. yesterday First seen · 100 lines · 93 tokens per session scan A 18bbbefb22f1

Subscribe to this mod's changes

agentify is a skill published in the GitHub repository tokencanopy/e2a (184 stars, last pushed yesterday), licensed Apache-2.0. It adds 93 tokens to every session and 1,462 once invoked, about $0.0005 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.

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