Agent skill

Ad Level Up

by CorridorTech in CorridorTech/PoseCap

Curate the project's rule-set — add, refine, merge, or retire a convention.

Apache-2.0Auto-check: notesAgent Workflows

Install Ad Level Up

skills CLI
$ npx skills add CorridorTech/PoseCap --skill ad-level-up -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install CorridorTech/PoseCap ad-level-up --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/CorridorTech/PoseCap.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/ad-level-up .claude/skills/ad-level-up && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ad-level-up
GitHub stars
220
Token cost
~2.1k tokens
SKILL.md length
1,041 words
Files
1
Skills in repo
15
Repo updated
First seen
Licence
Apache-2.0

At a glance

Curate the project's rule-set — add, refine, merge, or retire a convention.

  • Works in 8 steps: State candidate + evidence → Trace to root cause → Four anti-overfitting gates → …
  • Tasks that involve Plain language and style rules
  • SKILL.md covers Prime directive — HARD human…, Rules target, Step 1 — State candidate +… and Step 2 — Trace to root cause, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Ad Level Up is an agent skill from CorridorTech/PoseCap. Curate the project's rule-set — add, refine, merge, or retire a convention. The companion to ad-audit — where ad-audit audits against the rules, this evolves them, leanly. Every candidate must clear four anti-overfitting gates (recurrence-or-deliberate-decision, generalisation, load-bearing root cause, proportionate cost) plus an effectiveness pass (is it a genuine improvement, does it duplicate an existing rule, does it catch real observed behaviour) or it is rejected out loud. Every drafted candidate then…

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Plain language and style rules, Root cause analysis and Human-in-the-loop approvals. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Plain language and style rules
  • Tasks that involve Root cause analysis
  • Tasks that involve Human-in-the-loop approvals

Example prompts

  • “add a convention”
  • “update the rules”
  • “new rule”
  • “/ad-level-up”

Requirements

  • Pre-approved tools (allowed-tools): Read, Glob, Grep, Bash, Task, Edit, Write

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. State candidate + evidence
  2. Trace to root cause
  3. Four anti-overfitting gates
  4. Effectiveness pass
  5. Deterministic placement
  6. Draft the minimal edit
  7. Adversarial multi-lens review of the candidate
  8. Report + gate

What it can do on your machine

Read from SKILL.md and the folder at commit 626701b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Glob
    • Grep
    • Bash
    • Task
    • Edit
    • Write

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ad Level Up loads about 2.1k tokens when it runs. Until then it costs about 256 tokens; SKILL.md has 1,041 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~256
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Glob, Grep, Bash, Task, Edit, Write

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from CorridorTech/PoseCap at commit 626701b, republished under its Apache-2.0 licence (© CorridorTech). 1,041 words, ~2,113 tokens.

Download SKILL.mdSave it as .claude/skills/ad-level-up/SKILL.md (or your agent's skills folder).
name
ad-level-up
description
Curate the project's rule-set — add, refine, merge, or retire a convention. The companion to `ad-audit` — where ad-audit audits against the rules, this evolves them, leanly. Every candidate must clear four anti-overfitting gates (recurrence-or-deliberate-decision, generalisation, load-bearing root cause, proportionate cost) plus an effectiveness pass (is it a genuine improvement, does it duplicate an existing rule, does it catch real observed behaviour) or it is rejected out loud. Every drafted candidate then passes an adversarial multi-lens review — is it already covered? does it hold up? is it placed right? — and only survivors reach you. HARD human-in-the-loop — it NEVER writes without explicit approval; it presents a proposal with a plain-language rationale and applies only on your OK, one item at a time. Use on "add a convention", "update the rules", "new rule", "merge these rules", "we keep hitting X, make it a rule", "retire this rule", "/ad-level-up", or a rule-gap handoff from `ad-audit`.
allowed-tools
Read, Glob, Grep, Bash, Task, Edit, Write
summary
Human-gated rule-set curation, companion to ad-audit. Four anti-overfitting gates + effectiveness pass + adversarial multi-lens review of each candidate, then…

/ad-level-up

Evolve the rule-set without bloating it. Where ad-audit audits work against the rules, this skill curates the rules — adding only the generalising survivors, leanly, with evidence, and never without your explicit approval.

Prime directive — HARD human gate

A full or drifting context, or a model having an off moment, must never edit the rule-set unsupervised — that is exactly when a bad write slips in. So: NEVER write without explicit human approval. Always present the proposal plus a plain-language rationale first; apply only on the user's OK, one item at a time. No batch writes, no "while I'm here" edits. This gate is enforced by this contract, not by tool permissions — hold to it even though Edit is available.

Rules target

Two curated layers exist (ADR-0035 + ADR-0043); every accepted candidate targets exactly one:

  • Machine store — a you-everywhere convention: $AGENTIC_RULES_DIR if set, else ~/.agentic/rules/.
  • Project rules — a this-project convention: .agentic/rules/ at the repo root. On genuine conflict, a project rule shadows a machine-store rule (the audit reports the shadowing), so curate a project rule when the project deliberately deviates from the practitioner's global set.

Recommend the layer from the rule's own content (does it generalize beyond this repo?); the user confirms. Read the target layer before proposing (the edit needs a prior read; voice-matching needs the current text). If the target layer does not exist yet, say so — offer to create it on approval, rather than inventing a path. First project-rule creation: ask whether .agentic/rules/ is committed (versions with the repo; the team inherits it) or machine-local; in machine-local mode, on approval, write the .agentic/rules/ entry into .git/info/exclude yourself — never .gitignore, which is committed and team-visible. When you find an .agentic/rules/ that is neither committed nor excluded (e.g. a fresh clone), re-offer the choice. A convention that belongs in a repo binding doc (AGENTS.md, GUIDELINES.md) or is bigger than a rule line routes to /ad-adr or /ad-guidelines instead — this skill owns the terse rule-set.

Step 1 — State candidate + evidence

One sentence plus the citation (a finding, PR, transcript, or file:line — or the ad-audit handoff that surfaced it). If it cannot be cited, stop — it is not grounded, and a lone uncited slip is a memory note, not a rule.

Step 2 — Trace to root cause

Where was this seeded? Attach the candidate to the upstream cause (an investigation / grounding / verification gap), not the surface symptom.

Step 3 — Four anti-overfitting gates

All four must pass; reject the rest out loud:

  1. Recurrence or deliberate decision — it recurs / matches a known prior pattern, OR it is a deliberate forward-looking standardisation. A lone accidental slip with neither is a memory note, not a rule.
  2. Generalisation — it helps a whole class of future work, not just re-prevents this exact scenario.
  3. Load-bearing root cause — an upstream cause that cascaded, not a leaf symptom.
  4. Proportionate cost — it earns its keep against every reader carrying it forever (each extra rule lowers adherence to all the others). A rule preventing a rare, cheaply-caught-downstream slip is a net negative — reject it.

Step 4 — Effectiveness pass

  • Classify: improvement · correction · increment · merge into an existing rule · extend an existing rule · reject. Prefer sharpening / merging / extending over adding a new line.
  • Redundancy: does an existing rule (or a repo binding doc) already watch this? If so, merge — never add an overlapping rule.
  • Real behaviour: does it catch a defect actually observed (cite it), or a hypothetical? A rule guarding a defect nobody has hit is dead weight — reject or shelve as a note.
  • Dead-rule sweep: if an existing rule no longer maps to real behaviour or is subsumed by another, flag it for retirement in the same proposal.
Show full SKILL.md (433 more words)Show less

Step 5 — Deterministic placement

Place by grounding-target / failure-mode, never by nearest heading. Assign the rule to the group whose grounding-target it matches. Keep the group set minimal — a NEW group only when a defect class is demonstrably uncovered by every existing group; split a group only when its rule count grows too large (then split along its grounding-target). Give the rule the next stable id in its group.

Step 6 — Draft the minimal edit

Read the target rule file(s) first. Write the terse imperative rule under its group, and — when there is a why worth keeping — its rationale alongside. One rule, lean. Do not write to disk yet.

Step 7 — Adversarial multi-lens review of the candidate

Fan out fresh-context reviewers (one Task call per lens, routing to the bundled rule-candidate-reviewer), each trying to REFUTE the candidate against the ACTUAL current rule files — never assume. Run all three lenses.

  • (a) already-covered — is this rule or its effect already enforced by an existing rule / binding doc? Cite the covering rule. If covered, the disposition is reject (fully covered) or merge (sharpen the existing rule).
  • (b) coherence + necessity — does it hold up? Is it the minimal change? Does it clear the four gates and the placement principles?
  • (c) placement — is the group / id right, or does it belong elsewhere?

The review filters and reclassifies (survive-as-add / merge / reject); it never writes and never bypasses the human gate.

Step 8 — Report + gate

Present:

  • the proposed delta (exact old → new per file, at the resolved rule-set path);
  • the gate table (candidate · recurrence · generalisation · root-cause · cost · verdict);
  • the effectiveness line (classification · redundancy · real-behaviour · dead-rule sweep result);
  • the adversarial-review verdicts per lens (citing the covering rule on any reject/merge);
  • a plain-language rationale (one short paragraph);
  • a "considered but rejected as overfitting" list, each with the gate it failed.

Apply only on the user's explicit approval, one item at a time. On approval, make the minimal change — Edit an existing rule file, or Write to create the rule-set file at the resolved location when it does not exist yet; then stop and await the next item.

Output contract

  • A proposal (delta + gate table + effectiveness line + adversarial verdicts + rationale + rejected-as-overfitting list) — presented, not applied.
  • One Task invocation of rule-candidate-reviewer per lens.
  • On explicit approval: the minimal edit to the rule-set at the ADR-0035 location, one item at a time. Nothing written without approval.

Next

  • Re-run /ad-audit after a rule lands to confirm it catches what it was written for.
  • For a decision bigger than a rule line: /ad-adr (architectural) or /ad-guidelines (engineering standards).
  • ad-audit's Step 8 handoff is this skill's Step 1 candidate.

© CorridorTech, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/ad-level-up of CorridorTech/PoseCap.

Open the folder on GitHubat commit 626701b

Compare with similar skills

Ad Level Up next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Ad Level Up compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ad Level Up this skillCorridorTech/PoseCap220—~2.1kAutomated safety check: NotesApache-2.0
Failure CodifierChachamaru127/claude-code-harness3.2k—~603Automated safety check: NotesMIT
PUA High-Agency Governancetanweai/pua20k—~502Automated safety check: PassMIT
Issue BriefVasiHemanth/tokentelemetry376—~1.5kAutomated safety check: PassMIT
Defect Intake and RCA Candidatesprotect-my-hair/nucleus-marketplace164—~745Automated safety check: PassMIT
Paseo Committeegetpaseo/paseo20k1 repos~496Automated safety check: PassCustom licence

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More from CorridorTech/PoseCap

All 15 skills in this repo
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    Draft a new ADR (Architecture Decision Record) at doc/adr/NNNN-<short-title.md, using Michael Nygard's Context/Decision/Consequences/Alternatives pattern.

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  • Ad Architecture

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    Generate ARCHITECTURE.md at the repo root by scanning the code first, pre-filling layers/patterns/observability/deployment from observed signals, then asking only the genuine gaps.

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  • Ad Archive

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    Hard-delete completed plan files (tasks Status:done, specs Status:shipped, PRDs Status:superseded, ADRs Status:superseded or deprecated) via git rm, leaving git history as the only ledger.

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  • Ad Drift

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    Read-only drift audit — compare AGENTS.md, ARCHITECTURE.md, ADR statuses, feature specs in doc/specs/, and documentation discipline against what the code actually does.

    220 GitHub stars~1.8k tokensUpdated today
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  • Ad Hooks

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  • Ad Next

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    Survey the project's state across the six-layer artifact stack and recommend prioritized next actions, modeled on flutter doctor.

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Questions about Ad Level Up

What does Ad Level Up do?

Curate the project's rule-set — add, refine, merge, or retire a convention. Ad Level Up is an agent skill from CorridorTech/PoseCap. Curate the project's rule-set — add, refine, merge, or retire a convention.

When should I use Ad Level Up?

Ad Level Up fits situations like: tasks that involve Plain language and style rules; tasks that involve Root cause analysis; tasks that involve Human-in-the-loop approvals.

How do I install Ad Level Up in Claude Code?

Run `npx skills add CorridorTech/PoseCap --skill ad-level-up -a claude-code`. Or copy the skill folder (.claude/skills/ad-level-up in CorridorTech/PoseCap) into .claude/skills/ad-level-up in your project. Claude Code loads it when a task matches its description.

How do I install Ad Level Up in Codex?

Run `npx skills add CorridorTech/PoseCap --skill ad-level-up -a codex`. Or copy the skill folder (.claude/skills/ad-level-up in CorridorTech/PoseCap) into .agents/skills/ad-level-up in your project. Codex loads it when a task matches its description.

Can I use Ad Level Up in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add CorridorTech/PoseCap --skill ad-level-up -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ad-level-up, .gemini/skills/ad-level-up, .github/skills/ad-level-up and .opencode/skills/ad-level-up in your project.

What does Ad Level Up need to run?

SKILL.md names no scripts, command-line tools or credentials: Ad Level Up is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Glob, Grep, Bash, Task, Edit, Write.

Does Ad Level Up access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Ad Level Up safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Ad Level Up use?

Ad Level Up is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ad Level Up use?

About 2.1k tokens (SKILL.md is roughly 8.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Ad Level Up?

Skills that share tags, products or a category with Ad Level Up: Failure Codifier (Chachamaru127/claude-code-harness, 3.2k stars), PUA High-Agency Governance (tanweai/pua, 20k stars), Issue Brief (VasiHemanth/tokentelemetry, 376 stars) and Defect Intake and RCA Candidates (protect-my-hair/nucleus-marketplace, 164 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ad Level Up?

CorridorTech (a GitHub organization) maintains it in CorridorTech/PoseCap, which has 220 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on October 7, 2026.

Source: CorridorTech/PoseCap on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.