{"slug":"hkust-knowcomp-deeprefine-92359631","name":"deeprefine","description":"Agent-native DeepRefine refinement loop — same control flow as DeepRefine.refine(), graphify search instead of FAISS, session LLM, dry-run review before approved graph writes.","long_description":"---\nname: deeprefine\ndescription: >-\n  Agent-native DeepRefine refinement loop — same control flow as DeepRefine.refine(),\n  graphify search instead of FAISS, session LLM, dry-run review before approved graph writes.\ndisable-model-invocation: false\n---\n\n# DeepRefine — Agent refinement loop (strict)\n\n## Default safety policy: dry-run only\n\nA normal `/deeprefine` invocation **MUST NEVER** call `deeprefine apply`.\n\nThe default `/deeprefine` workflow must stop after:\n\n1. `deeprefine loop validate`\n2. `deeprefine review`\n3. showing the proposed actions and HIGH/MEDIUM/LOW review report to the user\n\nThen ask the user for explicit approval.\n\nOnly if the user's **next message** explicitly says to approve/apply/write the graph may you run:\n\n```bash\ndeeprefine apply --refresh-wiki --trace-file ... --refinement-file ...\ndeeprefine loop finish --trace-file ... --refinement-file ...\n```\n\nDo not treat generation of `<refinement>` actions as approval. Do not treat a valid trace as approval. Do not apply in the same `/deeprefine` turn.\n\n---\n\nYou **MUST** implement the **same control flow** as `DeepRefine.refine()` in DeepRefine (`autorefiner/src/deeprefine.py`).\n\n| Component | Agent mode | CLI `deeprefine refine` |\n|-----------|------------|-------------------------|\n| Retrieval | `graphify query` + k-hop from `graph.json` | FAISS retriever |\n| LLM | **Your session model** | External API / vLLM |\n| Graph writes | Dry-run proposal + `deeprefine review`; `deeprefine apply` only after user approval | Dry-run by default; `--apply` persists |\n\n---\n\n## FORBIDDEN (hard stop)\n\nDo **NOT**:\n\n1. Run `deeprefine refine` (unless the user explicitly asks for CLI/FAISS mode).\n2. Call `deeprefine apply` without a valid `loop_trace_<query_id>.json` (CLI will reject).\n3. Call `deeprefine apply` before running `deeprefine review` and receiving explicit user approval.\n4. Ignore LOW-confidence review warnings unless the user explicitly requests `--allow-low-confidence`.\n5. Skip any hop’s `<judge>Yes</judge>` / `<judge>No</judge>` judgement.\n6. Skip error abduction when `len(interaction_history) > 1`.\n7. Write `<refinement>` before abduction when refinement is required.\n8. Hand-edit `graph.json` with Python or ad-hoc JSON patches.\n9. Ignore pending history and refine only one latest query when unrefined queries already exist.\n10. Invent a shorter pipeline (“read file → write refinement → apply”).\n\nIf validation fails, **fix the trace and re-run the missing step** — do not bypass with `--skip-trace-check`.\n\n---\n\n## Constants (match `refine_runner.py` / DeepRefine)\n\n```text\nMAX_HOPS = 4\nINCREMENT_HOP = 1\nBASE_TOP_K = 10\nMAX_TRIPLE_NUM_BY_STEP = [5, 10, 15, 20]   # cap triples per step\nHISTORY_HORIZON = 4                        # abduction uses last N steps\n```\n\n---\n\n## Mandatory artifact\n\nFor each query, maintain:\n\n`graphify-out/.deeprefine/loop_trace_<query_id>.json`\n\nCreate template:\n\n```bash\ndeeprefine loop init --query \"<exact question>\"\n```\n\nAppend each hop to `interaction_history` **before** starting the next hop.  \nRun `deeprefine loop validate --trace-file ...` after abduction and before `review` / approved `apply`.\n\n---\n\n## Query queue selection (default behavior of `/deeprefine`)\n\n`/deeprefine` must process **all unrefined history queries** first, not just the latest one.\n\n1. Sync graphify query memory into DeepRefine history:\n   - run: `deeprefine history sync-memory`\n   - source dir: `graphify-out/memory/query_*.md`\n   - target file: `graphify-out/.deeprefine/history.jsonl`\n2. Read pending queue from `graphify-out/.deeprefine/history.jsonl`:\n   - include rows where `refined != true`\n   - dedupe by `id` (first occurrence)\n   - preserve file order\n3. If pending queue is non-empty: set `target_queries = pending_queue`.\n4. If pending queue is empty: set `target_queries = [current session question]`.\n5. Run the full refinement loop for **each** query in `target_queries`, one by one.\n6. For early-exit queries, finish immediately. For refinement-path queries, generate review output and wait for user approval before `apply` + `loop finish`.\n\n---\n\n## Control flow (must match `DeepRefine.refine()`)\n\nPseudocode — follow **exactly**:\n\n```text\ntarget_queries = pending_history_queries()  # refined != true, dedupe by id, keep order\nrun \"deeprefine history sync-memory\" before loading pending history\nif target_queries is empty:\n    target_queries = [current session question]\n\nfor question in target_queries:\n    interaction_history = []\n    for step in 1..MAX_HOPS:\n        print \"[Step: {step}]\"   # show in chat\n\n        if step == 1:\n            # Vector-retrieval equivalent: graphify query on full question\n            RUN: graphify query \"<question>\"\n            triples = parse NODE/EDGE → [{subject, relation, object}, ...]\n            cap = MAX_TRIPLE_NUM_BY_STEP[0]  # 5\n            record retrieval.method = \"graphify_query\"\n        else:\n            # k-hop expansion from entities in previous hop (NOT a new random search)\n            entities = unique subjects/objects from previous triples\n            expand 1-hop neighbors from graphify-out/graph.json (or graphify query on entities)\n            cap = MAX_TRIPLE_NUM_BY_STEP[step-1]\n            record retrieval.method = \"k_hop_expansion\" or \"graphify_query+k_hop_expansion\"\n\n        triples = dedupe; len(triples) <= cap\n\n        # Answerable judgement — session LLM, prompts below\n        answerable, judgement_raw = LLM_judge(question, triples)\n        MUST output ONLY: <judge>Yes</judge> or <judge>No</judge>\n\n        append interaction_history with:\n          step, query, num_hops=(step-1)*INCREMENT_HOP, base_top_k=10,\n          retrieved_subgraph, answerable, judgement_raw,\n          retrieval: {method, evidence: \"<command output excerpt>\"}\n\n        if answerable:\n            BREAK   # stop hop loop\n\n    # --- same branch as DeepRefine.refine() line 314+ ---\n    if len(interaction_history) <= 1:\n        # Early exit: first hop was answerable — NO graph refinement\n        set trace.early_exit = true\n        deeprefine loop finish --trace-file ...   # no --refinement-file\n        CONTINUE  # move to next pending query\n    else:\n        # len > 1 → ALWAYS error abduction + actions (even if last hop was Yes)\n        error_abduction = LLM_abduction(interaction_history[-HISTORY_HORIZON:])\n        MUST output: <abduction>...</abduction>\n\n        actions = LLM_kg_refinement(\n            last_hop.retrieved_subgraph,\n            error_abduction,\n            question,\n            source file hints from triples,\n        )\n        MUST output: <refinement>insert_edge(...)|...</refinement>\n\n        save refinement to graphify-out/.deeprefine/refinement_actions_<id>.txt\n        deeprefine loop validate --trace-file ... --refinement-file ...\n        deeprefine review --trace-file ... --refinement-file ...\n        SHOW review labels: HIGH / MEDIUM / LOW, evidence, warnings, suggested replacements\n        HARD STOP: do not modify graph.json in this /deeprefine turn\n        Report the proposed actions and HIGH/MEDIUM/LOW review to the user\n        Ask for explicit approval; approval must arrive in the user's next message\n\n        # Follow-up turn only, after the user's next message explicitly approves/apply/write:\n        if user explicitly approves and no LOW-confidence action remains:\n            deeprefine apply --refresh-wiki --trace-file ... --refinement-file ...\n            deeprefine loop finish --trace-file ... --refinement-file ...\n        if user explicitly accepts LOW-confidence risk in that approval message:\n            deeprefine apply --refresh-wiki --allow-low-confidence --trace-file ... --refinement-file ...\n            deeprefine loop finish --trace-file ... --refinement-file ...\n```\n\n**Critical refinement rule:** refinement runs when `len(interaction_history) > 1`, not only when all judgements are `No`.\n\n**Safe-review rule:** a refinement path is dry-run by default. Generating `<refinement>` actions is not approval to write `graph.json`, and a normal `/deeprefine` turn must end after `deeprefine review`.\n\n---\n\n## Evidence-aware review rules\n\nBefore any graph write, run:\n\n```bash\ndeeprefine review --trace-file graphify-out/.deeprefine/loop_trace_<id>.json --refinement-file graphify-out/.deeprefine/refinement_actions_<id>.txt\n```\n\nThe review must label every action:\n\n- `HIGH`: direct graph or code evidence exists.\n- `MEDIUM`: k-hop context supports the action, but direct code or exact-edge evidence is missing.\n- `LOW`: endpoint nodes are ambiguous, too broad, cross-community, or cannot be grounded in `graph.json`.\n\nBare names such as `main()`, `run()`, `train()`, `test()`, and `setup()` are ambiguous even if they match only one node. Prefer file-qualified labels such as `trainer_Brain_CLS.py::train_epoch()`.\n\n`deeprefine apply` refuses LOW-confidence actions by default. Use `--allow-low-confidence` only when the user explicitly approves the risk.\n\n---\n\n## LLM prompts (verbatim — do not paraphrase)\n\n### Judgement (`_answerable_judgement`)\n\n**System:**\n\n```text\nAs an advanced judgement assistant, your task is to judge whether the given question is answerable based on the provided KG context.\n\nEvaluate whether the given question is answerable based on the provided KG context. Output your judgment in the following format:\n<judge>Yes</judge> or <judge>No</judge>\n\n**Important:** You must think carefully about the question and the KG context before making your judgment. And output your judgment result directly in the specified format.\n```\n\n**User:**\n\n```text\nQuestion: {question}\nKnowledge Graph (KG) context: {triples_string}\n```\n\n`{triples_string}` = one triple per line: `subject | relation | object`\n\n### Error abduction (`_error_abduction`) — only if `len(interaction_history) > 1`\n\n**System:**\n\n```text\nAs an advanced error abduction assistant, your task is to analyze the error reasons based on the given interaction history.\n\nAnalyze the reasons of the unanswerable questions based on the given interaction history from the incompleteness, incorrectness, and redundancy perspectives. Output your analysis in the following format:\n<abduction>...</abduction>\n\n**Important:** You must think carefully about the interaction history before making your analysis. And output your analysis result directly in the specified format.\n```\n\n**User:**\n\n```text\nInteraction history: {interaction_history}\n```\n\n`{interaction_history}` format (same as DeepRefine):\n\n```text\nStep1:\n['Query': ..., 'Subgraph_hop': ..., 'Subgraph_content': ..., 'Answerable': ...]\n\nStep2:\n...\n```\n\n### KG refinement actions (`_kg_refinement_action`) — only if `len(interaction_history) > 1`\n\n**System:**\n\n```text\nAs an advanced knowledge graph refinement assistant, your task is to generate a series of actions (**within 10 actions**) to refine the given KG to make it more suitable for answering the given question.\n\nBased on the given KG and the analysed error reasons, refine the given KG to make it more easily for retrieval and answering the given question. You have the following three types of actions to conduct:\n\n- insert_edge(subject, relation, object): Insert a new edge into the KG to complete the missing information.\n- delete_edge(subject, relation, object): Delete an edge from the KG to remove the redundant information or conflicting information.\n- replace_node(old_entity, new_entity): Replace an entity in the KG to correct the errors or deal with disambiguation.\n\nOutput a series of actions (**within 10 actions**) in the following format:\n<refinement>insert_edge(\"...\", \"...\", \"...\")|delete_edge(\"...\", \"...\", \"...\")|replace_node(\"...\", \"...\")|...</refinement>\n\n**Important:** You must think carefully about the given KG and the analysed error reasons before making your refinement. DO NOT DELETE ANY IRRELEVANT TRIPLES FROM THE ORIGINAL KG. TRY TO KEEP THE ORIGINAL KG AS MUCH AS POSSIBLE. DO NOT GENERATE TOO MANY ACTIONS. And output your refinement result directly in the specified format.\n```\n\n**User:**\n\n```text\nOriginal Te","tagline":"Agent-native DeepRefine refinement loop — same control flow as DeepRefine.refine(), graphify search instead of FAISS, session LLM, dry-run review before approve","category":"automation","commerce":{"type":"unknown","billing":"unknown","amount":null,"currency":null,"sourceUrl":null,"checkedAt":null,"runtime":"unknown","purchaseUrl":null,"checkout":"external","purchaseRequiresUserConsent":true},"tags":["agent-skill"],"author":"HKUST-KnowComp","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"github candidate review","sourceDetail":"HKUST-KnowComp/DeepRefine-Skill","creatorName":"HKUST-KnowComp","creatorUrl":"https://github.com/HKUST-KnowComp","sourceUrl":"https://github.com/HKUST-KnowComp/DeepRefine-Skill/blob/main/SKILL.md","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/hkust-knowcomp-deeprefine-92359631#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals."},"stats":{"stars":92,"forks":7,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":37.33},"quality":{"score":64,"tier":"promising","label":"Promising","summary":"Useful candidate, but compare it with alternatives before adopting.","signals":[{"label":"GitHub stars","value":"92","tone":"neutral"},{"label":"Freshness","value":"2mo ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"MIT","tone":"neutral"}],"warnings":["The SKILL.md excerpt is truncated mid-pseudocode, but the provided content is sufficient to assess the skill's design and safety."]},"trust":{"version":"trust-score-v5","score":59,"base_score":67,"outcome_confidence":0,"tier":"risk","label":"Do not auto-install","summary":"Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.","recommendedAction":"Choose a stronger alternative or inspect the source manually before any install attempt.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["59/100 Trust Score v5","67/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is missing","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":48,"weight":0.13,"status":"warn","detail":"92 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":43,"weight":0.08,"status":"warn","detail":"92 stars, 7 forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":88,"weight":0.14,"status":"pass","detail":"2mo since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"MIT"},{"id":"documentation","label":"README/SKILL.md completeness","score":76,"weight":0.14,"status":"info","detail":"Public metadata needs stronger README/SKILL.md context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":64,"weight":0.12,"status":"info","detail":"command execution surface, network or browser surface"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add HKUST-KnowComp/DeepRefine-Skill --skill deeprefine"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":48,"weight":0.07,"status":"warn","detail":"shell or command execution, filesystem or document access"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/HKUST-KnowComp/DeepRefine-Skill/blob/main/SKILL.md"},{"id":"review_status","label":"Review status","score":66,"weight":0.05,"status":"info","detail":"AI review data available"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"warn","label":"GitHub adoption","detail":"92 GitHub stars"},{"status":"warn","label":"Stars/forks activity","detail":"92 stars, 7 forks; 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issue activity unavailable in current metadata","Permission surface: shell or command execution, filesystem or document access","No real agent outcome reports yet","Human review required before unattended installation"],"evidence":{"stars":"92 GitHub stars","repoActivity":"92 stars, 7 forks","lastPushed":"2mo since push","license":"MIT","repository":"https://github.com/HKUST-KnowComp/DeepRefine-Skill/blob/main/SKILL.md","install":"The tracked source changed or could not be synchronized. Review the current source before installing.","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, filesystem or document access","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet","agentProvenScore":0,"outcomeConfidence":"0%","installPolicy":"human_review_before_install"},"installReadiness":{"ready":false,"command":null,"policy":"human_review_before_install","label":"Human review before install","notes":["The tracked source changed or could not be synchronized. Review the current source before installing.","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","2mo since push","Trust Score v5 requires review or sandbox-only use before install."]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["The SKILL.md excerpt is truncated mid-pseudocode, but the provided content is sufficient to assess the skill's design and safety.","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","GitHub adoption: 92 GitHub stars","Stars/forks activity: 92 stars, 7 forks; issue activity unavailable in current metadata"]},"outcomeEvidence":{"total":0,"successes":0,"failures":0,"notRelevant":0,"successRate":null,"installAttempts":0,"riskBlocked":0,"setupRequired":0,"installSuccessRate":null,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"recentSuccessRate":null,"recentFailureRate":null,"uniqueAgents":0,"agentProvenScore":0,"agentProvenLabel":"Needs first agent run","lastOutcomeAt":null,"label":"No agent outcome data yet"},"autoInstall":{"allowed":false,"sandboxRequired":true,"policy":"human_review_before_install","reason":"Compare alternatives before installing."},"outcome_loop":{"version":"openagentskill-agent-outcome-v4","required_after_install":true,"endpoint":"/api/agent/outcome","method":"POST","event_id_source":"feedback.event_id, install_receipt.resolve_event_id, or decision_packet.outcome_feedback.event_id","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"required_fields":["event_id","skill_slug","task"],"quality_fields":["task_success","output_quality","error_type","human_review_required","used_in_production","workspace","evidence_url","time_to_useful_ms","source_version"],"ranking_inputs_updated":["Trust Score v5 outcome confidence","Agent Proven Score","Resolve ranking task-fit evidence","Skill detail machine-readable metadata","Outcome leaderboard"]},"agent_contract":{"suited_tasks":["automation","agent-skill"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"install_command":null,"trust_score":59,"trust_version":"trust-score-v5","risk_level":"medium","do_not_use_when":["Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"],"before_install":["Read the audit page and machine-readable metadata.","Confirm the install command, license, and permission surface fit the workspace.","Get explicit human approval or choose an alternative before installing."],"after_run":["Report the outcome to /api/agent/outcome using the resolve event id.","Include output_quality, workspace, human_review_required, and evidence_url when available.","Re-resolve before broad production rollout."]},"bestFor":["automation","agent-skill"],"doNotUseFor":["Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"],"knownRisks":["The SKILL.md excerpt is truncated mid-pseudocode, but the provided content is sufficient to assess the skill's design and safety.","Quality score needs review","Permission surface needs review: shell or command execution, filesystem or document access","GitHub adoption: 92 GitHub stars","Stars/forks activity: 92 stars, 7 forks; issue activity unavailable in current metadata","Permission surface: shell or command execution, filesystem or document access"],"backward_compatible":{"trust_score_v4":{"version":"trust-score-v4","score":67,"tier":"review","label":"Manual review","summary":"Potentially useful, but at least one trust signal needs human inspection."}}},"trust_score_v5":{"version":"trust-score-v5","score":59,"base_score":67,"outcome_confidence":0,"tier":"risk","label":"Do not auto-install","summary":"Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.","recommendedAction":"Choose a stronger alternative or inspect the source manually before any install attempt.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["59/100 Trust Score v5","67/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is missing","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":48,"weight":0.13,"status":"warn","detail":"92 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":43,"weight":0.08,"status":"warn","detail":"92 stars, 7 forks; 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